Repository: C2QA/bosonic-qiskit (language: python)

Issue:
Upgrade Qiskit
Our current code is compatible with Qiskit v1.0.1. Current as of 10/15/2024 is v1.2.4. The Qiskit API has changed between 1.0.1 and 1.2.4, requiring changes in the bosonic-qiskit code.

PRs https://github.com/C2QA/bosonic-qiskit/pull/118 and https://github.com/C2QA/bosonic-qiskit/pull/119 help us get the code updated. I believe #119 includes / supersedes the changes in #118.

Unfortunately the bosonic-qiskit Github workflows weren't configured to run on PRs from forks, so we'll want to make sure to run those before merging.

To complete the work, we need to update requirements.txt and pyproject.toml to upgrade our dependencies (in particular Qiskit).

We could consider making a new branch to upgrade our dependencies, and merge #119  into that new branch. This should run the tests and tutorials to be sure they all still work as expected.

Once the tests pass and the upgrade merged, we should publish (tag) a new version so the updated package gets uploaded to PyPI.


Discussion / hints:

Fix patch:
diff --git a/c2qa/__init__.py b/c2qa/__init__.py
index c262eda..b924870 100644
--- a/c2qa/__init__.py
+++ b/c2qa/__init__.py
@@ -1,4 +1,5 @@
 """Top-level package for bosonic-qiskit."""
+
 # flake8: noqa
 
 from c2qa.circuit import CVCircuit
@@ -11,4 +12,3 @@
 import c2qa.parameterized_unitary_gate
 import c2qa.util
 import c2qa.wigner
-
diff --git a/c2qa/animate.py b/c2qa/animate.py
index 1fca744..4651c44 100644
--- a/c2qa/animate.py
+++ b/c2qa/animate.py
@@ -29,11 +29,11 @@ def animate_wigner(
     axes_steps: int = 200,
     processes: int = None,
     keep_state: bool = True,
-    noise_passes = None,
+    noise_passes=None,
     sequential_subcircuit: bool = False,
     draw_grid: bool = False,
     trace: bool = True,
-    bitrate: int = -1
+    bitrate: int = -1,
 ):
     """Animate the Wigner function at each step defined in the given CVCirctuit.
 
@@ -49,7 +49,7 @@ def animate_wigner(
         cbit ([type]): Classical bit to measure into, if performing Hadamard measure for use with cat states. Defaults to None.
         animation_segments (int, optional): Number of segments to split each gate into for animation. Defaults to 10.
         shots (int, optional): Number of simulation shots per frame. Defaults to 1.
-        file (str, optional): File path to save (supported formats include MP4 with ffmpeg installed, animated GIF, and APNG). 
+        file (str, optional): File path to save (supported formats include MP4 with ffmpeg installed, animated GIF, and APNG).
                               If None, return plot. Defaults to None.
         axes_min (int, optional): Minimum axes plot value. Defaults to -6.
         axes_max (int, optional): Maximum axes plot value. Defaults to 6.
@@ -60,7 +60,7 @@ def animate_wigner(
                                      False if each frame starts over from the beginning of the circuit.
                                      If True, it requires sequential simulation of each frame.
         noise_passes (list of Qiskit noise passes, optional): noise passes to apply
-        sequential_subcircuit (bool, optional): boolean flag to animate subcircuits as one gate (False) or as sequential 
+        sequential_subcircuit (bool, optional): boolean flag to animate subcircuits as one gate (False) or as sequential
                                                 gates (True). Defautls to False.
         draw_grid (bool, optional): True if grid lines should be drawn on plot. Defaults to False.
         trace (bool, optional):  True if qubits should be tracedfor each frame prior to calculating Wigner function. Defaults to True.
@@ -83,7 +83,7 @@ def animate_wigner(
             keep_state,
             noise_passes,
             sequential_subcircuit,
-            trace
+            trace,
         )
     else:
         w_fock, xvec = __discretize_wigner_without_measure(
@@ -96,7 +96,7 @@ def animate_wigner(
             axes_steps,
             noise_passes,
             sequential_subcircuit,
-            trace
+            trace,
         )
 
     # Remove None values in w_fock if simulation didn't produce results
@@ -135,11 +135,13 @@ def __discretize_wigner_with_measure(
     axes_steps: int = 200,
     processes: int = None,
     keep_state: bool = True,
-    noise_passes = None,
+    noise_passes=None,
     sequential_subcircuit: bool = False,
     trace: bool = True,
 ):
-    circuits = discretize_circuits(circuit, animation_segments, keep_state, qubit, cbit, sequential_subcircuit)
+    circuits = discretize_circuits(
+        circuit, animation_segments, keep_state, qubit, cbit, sequential_subcircuit
+    )
 
     # Calculate the Wigner functions for each frame
     if not processes or processes < 1:
@@ -150,7 +152,9 @@ def __discretize_wigner_with_measure(
     xvec = numpy.linspace(axes_min, axes_max, axes_steps)
 
     if keep_state:
-        w_fock = __simulate_wigner_with_state(circuits, qubit, cbit, xvec, shots, noise_passes, trace)
+        w_fock = __simulate_wigner_with_state(
+            circuits, qubit, cbit, xvec, shots, noise_passes, trace
+        )
     elif processes == 1:
         w_fock = []
         for circuit in circuits:
@@ -168,15 +172,23 @@ def __discretize_wigner_with_measure(
         results = pool.starmap(
             simulate_wigner,
             (
-                (circuit, xvec, shots, noise_passes, cbit is not None, trace or cbit is not None)
+                (
+                    circuit,
+                    xvec,
+                    shots,
+                    noise_passes,
+                    cbit is not None,
+                    trace or cbit is not None,
+                )
                 for circuit in circuits
             ),
         )
         pool.close()
         w_fock = [i[0] for i in results if i is not None]
-    
+
     return w_fock, xvec
 
+
 def __discretize_wigner_without_measure(
     circuit: CVCircuit,
     animation_segments: int = 10,
@@ -185,20 +197,21 @@ def __discretize_wigner_without_measure(
     axes_min: int = -6,
     axes_max: int = 6,
     axes_steps: int = 200,
-    noise_passes = None,
+    noise_passes=None,
     sequential_subcircuit: bool = False,
     trace: bool = True,
 ):
     statevector_label = "segment_"
 
     discretized, num_statevectors = discretize_single_circuit(
-        circuit=circuit, 
+        circuit=circuit,
         segments_per_gate=animation_segments,
         epsilon=discretize_epsilon,
         sequential_subcircuit=sequential_subcircuit,
         statevector_per_segment=True,
         statevector_label=statevector_label,
-        noise_passes=noise_passes)
+        noise_passes=noise_passes,
+    )
 
     xvec = numpy.linspace(axes_min, axes_max, axes_steps)
 
@@ -209,7 +222,7 @@ def __discretize_wigner_without_measure(
         statevector_label=statevector_label,
         num_statevectors=num_statevectors,
         noise_passes=noise_passes,
-        trace=trace
+        trace=trace,
     )
 
     return w_fock, xvec
@@ -267,7 +280,14 @@ def _animate(frame, *fargs):
     if frame == 0:
         fig.colorbar(cont, ax=ax)
 
-    time_text = ax.text(0.05, 0.95, "", horizontalalignment="left", verticalalignment="top", transform=ax.transAxes)
+    time_text = ax.text(
+        0.05,
+        0.95,
+        "",
+        horizontalalignment="left",
+        verticalalignment="top",
+        transform=ax.transAxes,
+    )
     time_text.set_text(f"Frame {frame}")
 
     if file:
@@ -275,7 +295,9 @@ def _animate(frame, *fargs):
         plt.savefig(f"{file}_frames/frame_{frame}.png")
 
 
-def __simulate_wigner_with_state(circuits, qubit, cbit, xvec, shots, noise_passes, trace):
+def __simulate_wigner_with_state(
+    circuits, qubit, cbit, xvec, shots, noise_passes, trace
+):
     """Simulate Wigner function, preserving state between iterations"""
     w_fock = []
     previous_state = None
@@ -308,4 +330,4 @@ def __simulate_wigner_with_state(circuits, qubit, cbit, xvec, shots, noise_passe
         )
         w_fock.append(fock)
 
-    return w_fock
\ No newline at end of file
+    return w_fock
diff --git a/c2qa/circuit.py b/c2qa/circuit.py
index 8aeb047..f46d8ec 100644
--- a/c2qa/circuit.py
+++ b/c2qa/circuit.py
@@ -3,7 +3,6 @@
 
 import numpy as np
 from qiskit import QuantumCircuit, QuantumRegister
-from qiskit.circuit.parametertable import ParameterTable
 from qiskit.quantum_info.operators.predicates import is_unitary_matrix
 import qiskit_aer.library.save_instructions as save
 
@@ -81,16 +80,6 @@ def merge(self, circuit: QuantumCircuit):
 
         instr_copies = {id_: instr.copy() for id_, instr in instr_instances.items()}
 
-        self._parameter_table = ParameterTable(
-            {
-                param: [
-                    (instr_copies[id(instr)], param_index)
-                    for instr, param_index in circuit._parameter_table[param]
-                ]
-                for param in circuit._parameter_table
-            }
-        )
-
         self._data = [
             (instr_copies[id(inst)], qargs.copy(), cargs.copy())
             for inst, qargs, cargs in circuit._data
@@ -98,7 +87,7 @@ def merge(self, circuit: QuantumCircuit):
 
         self._calibrations = copy.deepcopy(circuit._calibrations)
         self._metadata = copy.deepcopy(circuit._metadata)
-    
+
     def get_qmr_cutoff(self, qmr_index: int):
         """Return the qumode cutoff at the given index"""
         return self.qmregs[qmr_index].cutoff
@@ -131,7 +120,7 @@ def qumode_qubit_indices(self):
     def qumode_qubits_indices_grouped(self):
         """Same as qumode_qubit_indices but it groups qubits representing the same qumode together. Returns a nested list."""
         grouped_indices = []
-    
+
         # Iterate through all qmregs
         for _, qmreg in enumerate(self.qmregs):
             num_qumodes_in_reg = qmreg.num_qumodes
@@ -140,18 +129,22 @@ def qumode_qubits_indices_grouped(self):
             qmreg_qubit_indices = []
 
             # For every qubit in circuit, append index of qubit to list if qubit is in qmreg
-            for qubit_index, qubit in enumerate(self.qubits): 
+            for qubit_index, qubit in enumerate(self.qubits):
                 if qubit in qmreg[:]:
                     qmreg_qubit_indices.append(qubit_index)
 
             # Split list according to no. of qumodes in qmreg
-            qmreg_qubit_indices = [qmreg_qubit_indices[i * num_qubits_per_qumode: (i + 1) * num_qubits_per_qumode] for i in range(num_qumodes_in_reg)]
+            qmreg_qubit_indices = [
+                qmreg_qubit_indices[
+                    i * num_qubits_per_qumode : (i + 1) * num_qubits_per_qumode
+                ]
+                for i in range(num_qumodes_in_reg)
+            ]
 
             # Extend final list
-            grouped_indices.extend(qmreg_qubit_indices) 
+            grouped_indices.extend(qmreg_qubit_indices)
 
-        return(grouped_indices)        
-         
+        return grouped_indices
 
     def get_qubit_index(self, qubit):
         """Return the index of the given Qubit"""
@@ -168,13 +161,13 @@ def get_qubit_indices(self, qubits: list):
                 flat_list += el
             else:
                 flat_list += [el]
-       
+
         indices = []
         for i, q in enumerate(self.qubits):
             if q in flat_list:
                 indices.append(i)
         return indices
-    
+
     def get_qmr_index(self, qubit):
         """Return the qumode index for the given qubit. If not found, return -1."""
         for index, qmr in enumerate(self.qmregs):
@@ -241,7 +234,9 @@ def cv_initialize(self, params, qumodes):
                 if params >= self.get_qmr_cutoff(qumode_index):
                     raise ValueError("The given Fock state is greater than the cutoff.")
 
-                value = np.zeros((self.get_qmr_cutoff(qumode_index),), dtype=np.complex_)
+                value = np.zeros(
+                    (self.get_qmr_cutoff(qumode_index),), dtype=np.complex128
+                )
                 value[params] = 1 + 0j
 
                 super().initialize(value, qumode)
@@ -253,7 +248,9 @@ def cv_initialize(self, params, qumodes):
                     raise ValueError("len(params) exceeds the cutoff.")
 
                 params = np.array(params) / np.linalg.norm(np.array(params))
-                amplitudes = np.zeros((self.get_qmr_cutoff(qumode_index),), dtype=np.complex_)
+                amplitudes = np.zeros(
+                    (self.get_qmr_cutoff(qumode_index),), dtype=np.complex128
+                )
                 for ind in range(len(params)):
                     amplitudes[ind] = complex(params[ind])
 
@@ -278,7 +275,13 @@ def cv_r(self, theta, qumode, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.r, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode), label="R", duration=duration, unit=unit
+                self.ops.r,
+                [theta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode),
+                label="R",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode,
         )
@@ -296,7 +299,13 @@ def cv_d(self, alpha, qumode, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.d, [alpha], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode), label="D", duration=duration, unit=unit
+                self.ops.d,
+                [alpha],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode),
+                label="D",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode,
         )
@@ -313,7 +322,13 @@ def cv_sq(self, theta, qumode, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.s, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode), label="S", duration=duration, unit=unit
+                self.ops.s,
+                [theta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode),
+                label="S",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode,
         )
@@ -331,7 +346,16 @@ def cv_sq2(self, theta, qumode_a, qumode_b, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.s2, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode_a)), QumodeRegister.calculate_cutoff(len(qumode_b))], num_qubits=len(qumode_a) + len(qumode_b), label="S2", duration=duration, unit=unit
+                self.ops.s2,
+                [theta],
+                cutoffs=[
+                    QumodeRegister.calculate_cutoff(len(qumode_a)),
+                    QumodeRegister.calculate_cutoff(len(qumode_b)),
+                ],
+                num_qubits=len(qumode_a) + len(qumode_b),
+                label="S2",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode_a + qumode_b,
         )
@@ -351,11 +375,14 @@ def cv_bs(self, theta, qumode_a, qumode_b, duration=100, unit="ns"):
             ParameterizedUnitaryGate(
                 self.ops.bs,
                 [theta],
-                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode_a)), QumodeRegister.calculate_cutoff(len(qumode_b))],
+                cutoffs=[
+                    QumodeRegister.calculate_cutoff(len(qumode_a)),
+                    QumodeRegister.calculate_cutoff(len(qumode_b)),
+                ],
                 num_qubits=len(qumode_a) + len(qumode_b),
                 label="BS",
                 duration=duration,
-                unit=unit
+                unit=unit,
             ),
             qargs=qumode_a + qumode_b,
         )
@@ -379,7 +406,7 @@ def cv_c_r(self, theta, qumode, qubit, duration=100, unit="ns"):
                 num_qubits=len(qumode) + 1,
                 label="cR",
                 duration=duration,
-                unit=unit
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -397,7 +424,13 @@ def cv_c_rx(self, theta, qumode, qubit, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.crx, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode) + 1, label="cRX", duration=duration, unit=unit
+                self.ops.crx,
+                [theta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="cRX",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -415,7 +448,13 @@ def cv_c_ry(self, theta, qumode, qubit, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.cry, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode) + 1, label="cRY", duration=duration, unit=unit
+                self.ops.cry,
+                [theta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="cRY",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -438,7 +477,13 @@ def cv_c_d(self, theta, qumode, qubit, beta=None, duration=100, unit="ns"):
 
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.cd, [theta, beta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode) + 1, label="cD", duration=duration, unit=unit
+                self.ops.cd,
+                [theta, beta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="cD",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -455,7 +500,13 @@ def cv_ecd(self, theta, qumode, qubit, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.ecd, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode) + 1, label="ECD", duration=duration, unit=unit
+                self.ops.ecd,
+                [theta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="ECD",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -476,16 +527,21 @@ def cv_c_bs(self, theta, qumode_a, qumode_b, qubit, duration=100, unit="ns"):
             ParameterizedUnitaryGate(
                 self.ops.cbs,
                 [theta],
-                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode_a)), QumodeRegister.calculate_cutoff(len(qumode_b))],
+                cutoffs=[
+                    QumodeRegister.calculate_cutoff(len(qumode_a)),
+                    QumodeRegister.calculate_cutoff(len(qumode_b)),
+                ],
                 num_qubits=len(qumode_a) + len(qumode_b) + 1,
                 label="cBS",
                 duration=duration,
-                unit=unit
+                unit=unit,
             ),
             qargs=qumode_a + qumode_b + [qubit],
         )
 
-    def cv_c_schwinger(self, params, qumode_a, qumode_b, qubit, duration=100, unit="ns"):
+    def cv_c_schwinger(
+        self, params, qumode_a, qumode_b, qubit, duration=100, unit="ns"
+    ):
         """General form of a controlled 'Schwinger' gate, containing both the controlled phase beamsplitter
         and pairs of controlled phase space rotations as special cases.
 
@@ -513,12 +569,15 @@ def cv_c_schwinger(self, params, qumode_a, qumode_b, qubit, duration=100, unit="
             ParameterizedUnitaryGate(
                 self.ops.cschwinger,
                 params,
-                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode_a)), QumodeRegister.calculate_cutoff(len(qumode_b))],
+                cutoffs=[
+                    QumodeRegister.calculate_cutoff(len(qumode_a)),
+                    QumodeRegister.calculate_cutoff(len(qumode_b)),
+                ],
                 num_qubits=len(qumode_a) + len(qumode_b) + 1,
                 label="cSchw",
                 duration=duration,
                 unit=unit,
-                discretized_param_indices=[0]
+                discretized_param_indices=[0],
             ),
             qargs=qumode_a + qumode_b + [qubit],
         )
@@ -540,46 +599,70 @@ def cv_snap(self, theta, n, qumode, qubit=None, duration=100, unit="ns"):
             Instruction: QisKit instruction
         """
         cutoff = QumodeRegister.calculate_cutoff(len(qumode))
-        if isinstance(n,int):
+        if isinstance(n, int):
             if n > cutoff:
                 ValueError("Fock state specified by n exceeds the cutoff.")
             if qubit is None:
                 self.append(
                     ParameterizedUnitaryGate(
-                        self.ops.snap, [theta, n], cutoffs=[cutoff], num_qubits=len(qumode), label="SNAP", duration=duration, unit=unit
+                        self.ops.snap,
+                        [theta, n],
+                        cutoffs=[cutoff],
+                        num_qubits=len(qumode),
+                        label="SNAP",
+                        duration=duration,
+                        unit=unit,
                     ),
                     qargs=qumode,
                 )
             else:
                 self.append(
                     ParameterizedUnitaryGate(
-                        self.ops.csnap, [theta, n], cutoffs=[cutoff], num_qubits=len(qumode) + 1, label="cSNAP", duration=duration, unit=unit
+                        self.ops.csnap,
+                        [theta, n],
+                        cutoffs=[cutoff],
+                        num_qubits=len(qumode) + 1,
+                        label="cSNAP",
+                        duration=duration,
+                        unit=unit,
                     ),
                     qargs=qumode + [qubit],
                 )
-        elif isinstance(n,list) and isinstance(theta,list):
+        elif isinstance(n, list) and isinstance(theta, list):
             if qubit is None:
                 self.append(
                     ParameterizedUnitaryGate(
-                        self.ops.multisnap, theta + n, cutoffs=[cutoff], num_qubits=len(qumode), label="SNAP", duration=duration, unit=unit
+                        self.ops.multisnap,
+                        theta + n,
+                        cutoffs=[cutoff],
+                        num_qubits=len(qumode),
+                        label="SNAP",
+                        duration=duration,
+                        unit=unit,
                     ),
                     qargs=qumode,
                 )
             else:
                 self.append(
                     ParameterizedUnitaryGate(
-                        self.ops.multicsnap, theta + n, cutoffs=[cutoff], num_qubits=len(qumode) + 1, label="cSNAP", duration=duration, unit=unit
+                        self.ops.multicsnap,
+                        theta + n,
+                        cutoffs=[cutoff],
+                        num_qubits=len(qumode) + 1,
+                        label="cSNAP",
+                        duration=duration,
+                        unit=unit,
                     ),
                     qargs=qumode + [qubit],
                 )
         else:
-            raise ValueError("if theta is passed as a list, then n must also be a list of equal length (and vice versa).")
-
+            raise ValueError(
+                "if theta is passed as a list, then n must also be a list of equal length (and vice versa)."
+            )
 
     # def cv_c_sqr(self, theta, n, qumode, qubit, duration=100, unit="ns"):
     #     """TODO"""
 
-            
     # def cv_multisnap(self, thetas, ns, qumode, duration=1, unit="us"):
     #     params = thetas + ns
     #     self.append(
@@ -588,9 +671,9 @@ def cv_snap(self, theta, n, qumode, qubit=None, duration=100, unit="ns"):
     #         ),
     #         qargs=qumode,
     #     )
-            
+
     def cv_c_pnr(self, max, qumode, qubit, duration=100, unit="ns"):
-        """ PNR (Photon number readout) TODO: Needs comments/explanation/citation!
+        """PNR (Photon number readout) TODO: Needs comments/explanation/citation!
         Args:
             max (int): the period of the mapping
             qumode (list): list of qubits representing qumode
@@ -600,7 +683,13 @@ def cv_c_pnr(self, max, qumode, qubit, duration=100, unit="ns"):
         """
         self.append(
             ParameterizedUnitaryGate(
-                self.ops.pnr, [max], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode) + 1, label="c_pnr", duration=duration, unit=unit
+                self.ops.pnr,
+                [max],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="c_pnr",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -620,11 +709,14 @@ def cv_eswap(self, theta, qumode_a, qumode_b, duration=100, unit="ns"):
             ParameterizedUnitaryGate(
                 self.ops.eswap,
                 [theta],
-                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode_a)), QumodeRegister.calculate_cutoff(len(qumode_b))],
+                cutoffs=[
+                    QumodeRegister.calculate_cutoff(len(qumode_a)),
+                    QumodeRegister.calculate_cutoff(len(qumode_b)),
+                ],
                 num_qubits=len(qumode_a) + len(qumode_b),
                 label="eSWAP",
                 duration=duration,
-                unit=unit
+                unit=unit,
             ),
             qargs=qumode_a + qumode_b,
         )
@@ -642,7 +734,13 @@ def cv_c_sq(self, theta, qumode, qubit, duration=100, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.csq, [theta], cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], num_qubits=len(qumode) + 1, label="cS", duration=duration, unit=unit
+                self.ops.csq,
+                [theta],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="cS",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
@@ -650,13 +748,13 @@ def cv_c_sq(self, theta, qumode, qubit, duration=100, unit="ns"):
     def cv_testqubitorderf(self, phi, qubit_1, qubit_2, duration=100, unit="ns"):
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.testqubitorderf, 
-                [phi], 
-                label="testqubitorderf", 
-                cutoffs=[], 
-                num_qubits=2, 
-                duration=duration, 
-                unit=unit
+                self.ops.testqubitorderf,
+                [phi],
+                label="testqubitorderf",
+                cutoffs=[],
+                num_qubits=2,
+                duration=duration,
+                unit=unit,
             ),
             qargs=[qubit_1] + [qubit_2],
         )
@@ -749,12 +847,12 @@ def cv_measure(self, qubit_qumode_list, cbit_list):
         # This piece is useful so that the user doesn't need to think about
         # how many bits are needed to read out a list of qumodes, qubits, etc.
         if len(flat_list) < len(cbit_list):
-            self.measure(flat_list, cbit_list[0:len(flat_list)])
+            self.measure(flat_list, cbit_list[0 : len(flat_list)])
         else:
             self.measure(flat_list, cbit_list)
 
     def cv_delay(self, duration, qumode, unit="ns"):
-        """CV_delay. Implements an identity gate of the specified duration. 
+        """CV_delay. Implements an identity gate of the specified duration.
         This is particularly useful for the implementation of a noise pass.
 
         Args:
@@ -766,13 +864,13 @@ def cv_delay(self, duration, qumode, unit="ns"):
         """
         return self.append(
             ParameterizedUnitaryGate(
-                self.ops.get_eye, 
-                [], 
+                self.ops.get_eye,
+                [],
                 cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
-                num_qubits=len(qumode), 
-                label="cv_delay(" + str(duration) + " " + unit +")", 
-                duration=duration, 
-                unit=unit
+                num_qubits=len(qumode),
+                label="cv_delay(" + str(duration) + " " + unit + ")",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode,
         )
@@ -788,20 +886,28 @@ def cv_c_multiboson_sampling(self, max, qumode, qubit, duration=1, unit="us"):
         """
         self.append(
             ParameterizedUnitaryGate(
-                self.ops.c_multiboson_sampling, 
-                [max], 
-                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))], 
-                num_qubits=len(qumode) + 1, 
-                label="c_multiboson_sampling", 
-                duration=duration, 
-                unit=unit
+                self.ops.c_multiboson_sampling,
+                [max],
+                cutoffs=[QumodeRegister.calculate_cutoff(len(qumode))],
+                num_qubits=len(qumode) + 1,
+                label="c_multiboson_sampling",
+                duration=duration,
+                unit=unit,
             ),
             qargs=qumode + [qubit],
         )
 
-    def cv_gate_from_matrix(self, matrix, qumodes=[], qubits=[], duration=100, unit="ns", label:str="cv_gate_from_matrix"):
-        """Converts matrix to gate. Note that if you choose to input some complex gate that would typically be physically 
-        implemented by multiple successive gate operations, PhotonLossNoisePass, simulate(discretize=True), and animate may 
+    def cv_gate_from_matrix(
+        self,
+        matrix,
+        qumodes=[],
+        qubits=[],
+        duration=100,
+        unit="ns",
+        label: str = "cv_gate_from_matrix",
+    ):
+        """Converts matrix to gate. Note that if you choose to input some complex gate that would typically be physically
+        implemented by multiple successive gate operations, PhotonLossNoisePass, simulate(discretize=True), and animate may
         not be applied in a way that is physical.
 
         Args:
@@ -811,26 +917,26 @@ def cv_gate_from_matrix(self, matrix, qumodes=[], qubits=[], duration=100, unit=
 
         Returns:
             Instruction: QisKit instruction
-        """     
+        """
         # If multiple qubits are given, slice to get list of qubits. Otherwise, encase QuantumRegister for single qubit in list.
-        try: 
+        try:
             qubits = qubits[:]
-        except: 
+        except:
             qubits = [qubits]
 
         # Slice QumodeRegister to get list of qumodes
-        if isinstance(qumodes, QumodeRegister):    
+        if isinstance(qumodes, QumodeRegister):
             qumodes = qumodes[:]
 
         # Convert matrix to np.ndarray so that we can compute error flags easier
         matrix = np.array(matrix)
-        
+
         ## Error flags
         # Matrix needs to be square
         n, m = matrix.shape
         if n != m:
             raise ValueError("Matrix given is not square")
-        
+
         # Determine if input matrix is same dimension as input qumodes+qubits
         if n != 2 ** (len(qumodes) + len(qubits)):
             raise ValueError("Matrix is of different dimension from qumodes + qubits")
@@ -850,14 +956,14 @@ def cv_gate_from_matrix(self, matrix, qumodes=[], qubits=[], duration=100, unit=
 
         # return self.append(
         #     ParameterizedUnitaryGate(
-        #         self.ops.gate_from_matrix, 
-        #         [matrix], 
+        #         self.ops.gate_from_matrix,
+        #         [matrix],
         #         cutoffs=cutoffs,
-        #         num_qubits=len(qumodes) + len(qubits), 
-        #         label=label, 
-        #         duration=duration, 
+        #         num_qubits=len(qumodes) + len(qubits),
+        #         label=label,
+        #         duration=duration,
         #         unit=unit
         #     ),
         #     qargs=qumodes + qubits,
         # )
-        return self.unitary(matrix, qubits=qumodes + qubits, label=label)
\ No newline at end of file
+        return self.unitary(matrix, qubits=qumodes + qubits, label=label)
diff --git a/c2qa/discretize.py b/c2qa/discretize.py
index bb253a8..ef8d940 100644
--- a/c2qa/discretize.py
+++ b/c2qa/discretize.py
@@ -7,12 +7,13 @@
 
 
 def discretize_circuits(
-        circuit: CVCircuit, 
-        segments_per_gate: int = 10, 
-        keep_state: bool = True, 
-        qubit:qiskit.circuit.quantumcircuit.QubitSpecifier = None, 
-        cbit: qiskit.circuit.quantumcircuit.QubitSpecifier = None, 
-        sequential_subcircuit: bool = False,):
+    circuit: CVCircuit,
+    segments_per_gate: int = 10,
+    keep_state: bool = True,
+    qubit: qiskit.circuit.quantumcircuit.QubitSpecifier = None,
+    cbit: qiskit.circuit.quantumcircuit.QubitSpecifier = None,
+    sequential_subcircuit: bool = False,
+):
     """
     Discretize gates into a circuit into segments where each segment ends an indiviudal circuit. Useful for incrementally applying noise or animating the circuit.
 
@@ -24,9 +25,9 @@ def discretize_circuits(
                                      If True, it requires sequential simulation of each segment.
         qubit ([QubitSpecifier]): Qubit to measure, if performing Hadamard measure for use with cat states. Defaults to None.
         cbit ([QubitSpecifier]): Classical bit to measure into, if performing Hadamard measure for use with cat states. Defaults to None.
-        sequential_subcircuit (bool, optional): boolean flag to animate subcircuits as one gate (False) or as sequential 
+        sequential_subcircuit (bool, optional): boolean flag to animate subcircuits as one gate (False) or as sequential
                                                 gates (True). Defautls to False.
-    
+
     Returns:
         [list]: List of discretized Qiskit circuit
     """
@@ -42,7 +43,9 @@ def discretize_circuits(
         # qubit = xxx
         # cbit = yyy
 
-        segments = __to_segments(inst, segments_per_gate, keep_state, sequential_subcircuit)
+        segments = __to_segments(
+            inst, segments_per_gate, keep_state, sequential_subcircuit
+        )
 
         for segment in segments:
             sim_circuit = base_circuit.copy()
@@ -58,19 +61,19 @@ def discretize_circuits(
 
             # Start with current circuit for the next segment
             base_circuit = sim_circuit
-    
+
     return sim_circuits
 
 
 def discretize_single_circuit(
-        circuit: CVCircuit, 
-        segments_per_gate: int = 10, 
-        epsilon: float = None,
-        sequential_subcircuit: bool = False,
-        statevector_per_segment: bool = False,
-        statevector_label: str = "segment_",
-        noise_passes = None
-    ):
+    circuit: CVCircuit,
+    segments_per_gate: int = 10,
+    epsilon: float = None,
+    sequential_subcircuit: bool = False,
+    statevector_per_segment: bool = False,
+    statevector_label: str = "segment_",
+    noise_passes=None,
+):
     """
     Discretize gates into a circuit into segments within a single output circuit. Useful for incrementally applying noise or animating the circuit.
 
@@ -79,14 +82,14 @@ def discretize_single_circuit(
         segments_per_gate (int, optional): Number of segments to split each gate into. Defaults to 10.
         epsilon (float, optional): float value used to discretize, must specify along with kappa
         kappa (float, optional): float phton loss rate to determine discretization sice, must specify along with epsilon
-        sequential_subcircuit (bool, optional): boolean flag to animate subcircuits as one gate (False) or as sequential 
+        sequential_subcircuit (bool, optional): boolean flag to animate subcircuits as one gate (False) or as sequential
                                                 gates (True). Defaults to False.
-        statevector_per_segment (bool, optional): boolean flag to save a statevector per gate segment. True will call Qiskit 
-                                                  save_statevector after each segment is simulated, creating statevectors labeled 
+        statevector_per_segment (bool, optional): boolean flag to save a statevector per gate segment. True will call Qiskit
+                                                  save_statevector after each segment is simulated, creating statevectors labeled
                                                   "segment_*" that can used after simulation. Defaults to False.
         statevector_label (str, optional): String prefix to use for the statevector saved after each segment
         noise_passes (list of Qiskit noise passes, optional): noise passes to apply
-    
+
     Returns:
         discretized Qiskit circuit
     """
@@ -102,33 +105,59 @@ def discretize_single_circuit(
     segment_count = 0
     for inst, qargs, cargs in circuit.data:
         num_segments = segments_per_gate
-        qargs_indices = [qubit._index for qubit in qargs]  # FIXME -- is there a public API to get the qubit's index in Qiskit v1.0+?
-
-        if noise_passes and not (isinstance(inst, qiskit.circuit.instruction.Instruction) and inst.name == "initialize"):  # Don't discretize instructions initializing system state:
+        qargs_indices = [
+            qubit._index for qubit in qargs
+        ]  # FIXME -- is there a public API to get the qubit's index in Qiskit v1.0+?
+
+        if noise_passes and not (
+            isinstance(inst, qiskit.circuit.instruction.Instruction)
+            and inst.name == "initialize"
+        ):  # Don't discretize instructions initializing system state:
             noise_pass = None
             for current in noise_passes:
-                if isinstance(current, PhotonLossNoisePass) and current.applies_to_instruction(inst, qargs_indices):
+                if isinstance(
+                    current, PhotonLossNoisePass
+                ) and current.applies_to_instruction(inst, qargs_indices):
                     noise_pass = current
                     break
-            
+
             if epsilon is not None and noise_pass is not None:
                 # FIXME - which of the qumodes' loss rates and QumodeRegister's cutoff should we use?
                 photon_loss_rate = noise_pass.photon_loss_rates_sec[0]
-                num_segments = math.ceil((photon_loss_rate * noise_pass.duration_to_sec(inst) * circuit.get_qmr_cutoff(0)) / epsilon)
-
-        segments = __to_segments(inst=inst, segments_per_gate=num_segments, keep_state=True, sequential_subcircuit=sequential_subcircuit)
+                num_segments = math.ceil(
+                    (
+                        photon_loss_rate
+                        * noise_pass.duration_to_sec(inst)
+                        * circuit.get_qmr_cutoff(0)
+                    )
+                    / epsilon
+                )
+
+        segments = __to_segments(
+            inst=inst,
+            segments_per_gate=num_segments,
+            keep_state=True,
+            sequential_subcircuit=sequential_subcircuit,
+        )
 
         for segment in segments:
             discretized.append(instruction=segment, qargs=qargs, cargs=cargs)
 
             if statevector_per_segment:
-                discretized.save_statevector(label=f"{statevector_label}{segment_count}")
+                discretized.save_statevector(
+                    label=f"{statevector_label}{segment_count}"
+                )
                 segment_count += 1
 
     return discretized, segment_count
 
 
-def __to_segments(inst: qiskit.circuit.instruction.Instruction, segments_per_gate: int, keep_state: bool, sequential_subcircuit: bool):
+def __to_segments(
+    inst: qiskit.circuit.instruction.Instruction,
+    segments_per_gate: int,
+    keep_state: bool,
+    sequential_subcircuit: bool,
+):
     """Split the instruction into segments_per_gate segments"""
 
     if isinstance(inst, ParameterizedUnitaryGate):
@@ -136,11 +165,23 @@ def __to_segments(inst: qiskit.circuit.instruction.Instruction, segments_per_gat
         segments = __discretize_parameterized(inst, segments_per_gate, keep_state)
 
     # FIXME -- how to identify a gate that was made with QuantumCircuit.to_gate()?
-    elif isinstance(inst.definition, qiskit.QuantumCircuit) and inst.name != "initialize" and inst.label != "cv_gate_from_matrix" and len(inst.decompositions) == 0:  # Don't animate subcircuits initializing system state
+    elif (
+        isinstance(inst.definition, qiskit.QuantumCircuit)
+        and inst.name != "initialize"
+        and inst.label != "cv_gate_from_matrix"
+        and len(inst.decompositions) == 0
+    ):  # Don't animate subcircuits initializing system state
         # print(f"Discretizing QuantumCircuit {inst.name}")
-        segments = __discretize_subcircuit(inst.definition, segments_per_gate, keep_state, sequential_subcircuit)
+        segments = __discretize_subcircuit(
+            inst.definition, segments_per_gate, keep_state, sequential_subcircuit
+        )
 
-    elif isinstance(inst, qiskit.circuit.instruction.Instruction) and inst.name != "initialize" and inst.label != "cv_gate_from_matrix" and len(inst.params) > 0:  # Don't animate instructions initializing system state
+    elif (
+        isinstance(inst, qiskit.circuit.instruction.Instruction)
+        and inst.name != "initialize"
+        and inst.label != "cv_gate_from_matrix"
+        and len(inst.params) > 0
+    ):  # Don't animate instructions initializing system state
         # print(f"Discretizing Instruction {inst.name}")
         segments = __discretize_instruction(inst, segments_per_gate, keep_state)
 
@@ -148,11 +189,16 @@ def __to_segments(inst: qiskit.circuit.instruction.Instruction, segments_per_gat
         # Else just "discretize" the instruction as a single segment
         # print(f"NOT discretizing {inst.name}")
         segments = [inst]
-    
+
     return segments
 
 
-def __discretize_parameterized(inst: qiskit.circuit.instruction.Instruction, segments_per_gate: int, keep_state: bool, discretized_param_indices: list = []):
+def __discretize_parameterized(
+    inst: qiskit.circuit.instruction.Instruction,
+    segments_per_gate: int,
+    keep_state: bool,
+    discretized_param_indices: list = [],
+):
     """Split ParameterizedUnitaryGate into multiple segments"""
     segments = []
     for index in range(1, segments_per_gate + 1):
@@ -184,14 +230,27 @@ def __discretize_parameterized(inst: qiskit.circuit.instruction.Instruction, seg
     return segments
 
 
-def __discretize_subcircuit(subcircuit: qiskit.QuantumCircuit, segments_per_gate: int, keep_state: bool, sequential_subcircuit: bool):
+def __discretize_subcircuit(
+    subcircuit: qiskit.QuantumCircuit,
+    segments_per_gate: int,
+    keep_state: bool,
+    sequential_subcircuit: bool,
+):
     """Create a list of circuits where the entire subcircuit is converted into segments (vs a single instruction)."""
 
     segments = []
     sub_segments = []
 
     for inst, qargs, cargs in subcircuit.data:
-        sub_segments.append((__to_segments(inst, segments_per_gate, keep_state, sequential_subcircuit), qargs, cargs))
+        sub_segments.append(
+            (
+                __to_segments(
+                    inst, segments_per_gate, keep_state, sequential_subcircuit
+                ),
+                qargs,
+                cargs,
+            )
+        )
 
     if sequential_subcircuit:
         # Sequentially animate each gate within the subcircuit
@@ -202,7 +261,7 @@ def __discretize_subcircuit(subcircuit: qiskit.QuantumCircuit, segments_per_gate
             gates, gate_qargs, gate_cargs = sub_segment
             for gate in gates:
                 subcircuit_copy.append(gate, gate_qargs, gate_cargs)
-        
+
         segments.append(subcircuit)
     else:
         # Animate the subcircuit as one gate
@@ -210,15 +269,19 @@ def __discretize_subcircuit(subcircuit: qiskit.QuantumCircuit, segments_per_gate
             subcircuit_copy = subcircuit.copy()
             subcircuit_copy.data.clear()  # Is this safe -- could we copy without data?
 
-            for sub_segment,  qargs, cargs in sub_segments:
+            for sub_segment, qargs, cargs in sub_segments:
                 subcircuit_copy.append(sub_segment[segment], qargs, cargs)
-            
+
             segments.append(subcircuit_copy)
 
-    return segments    
+    return segments
 
 
-def __discretize_instruction(inst: qiskit.circuit.instruction.Instruction, segments_per_gate: int, keep_state: bool):
+def __discretize_instruction(
+    inst: qiskit.circuit.instruction.Instruction,
+    segments_per_gate: int,
+    keep_state: bool,
+):
     """Split Qiskit Instruction into multiple segments"""
     segments = []
 
@@ -233,12 +296,12 @@ def __discretize_instruction(inst: qiskit.circuit.instruction.Instruction, segme
             total_steps=segments_per_gate,
             keep_state=keep_state,
         )
-        
+
         segments.append(
             qiskit.circuit.instruction.Instruction(
                 name=inst.name,
                 num_qubits=inst.num_qubits,
-                num_clbits = inst.num_clbits,
+                num_clbits=inst.num_clbits,
                 params=params,
                 duration=duration,
                 unit=unit,
@@ -246,4 +309,4 @@ def __discretize_instruction(inst: qiskit.circuit.instruction.Instruction, segme
             )
         )
 
-    return segments
\ No newline at end of file
+    return segments
diff --git a/c2qa/kraus.py b/c2qa/kraus.py
index a059e51..d595f24 100644
--- a/c2qa/kraus.py
+++ b/c2qa/kraus.py
@@ -15,11 +15,12 @@
 
 
 def calculate_kraus(
-    photon_loss_rates: Sequence[float], 
-    time: float, 
+    photon_loss_rates: Sequence[float],
+    time: float,
     circuit: c2qa.CVCircuit,
     op_qubits: Sequence[int],
-    qumode_qubit_indices: Sequence[int]):
+    qumode_qubit_indices: Sequence[int],
+):
     """
     Calculate Kraus operator given number of photons and photon loss rate over specified time.
 
@@ -50,12 +51,20 @@ def calculate_kraus(
             qmr_index = circuit.get_qmr_index(qubit)
             qumode_index = circuit.qmregs[qmr_index].get_qumode_index(qubit)
             unique_index = f"{qmr_index}-{qumode_index}"
-            
+
             cutoff = circuit.get_qmr_cutoff(qmr_index)
 
             # Tensor Kraus operators, if not already done
-            if not kraus_tensor.get(unique_index, False):  # FIXME need to tensor per qumode, not qumode register
-                kraus_ops = __kraus_operators(photon_loss_rates[loss_rate_index], time, cutoff, circuit.ops.get_a(cutoff), circuit.ops.get_N(cutoff))
+            if not kraus_tensor.get(
+                unique_index, False
+            ):  # FIXME need to tensor per qumode, not qumode register
+                kraus_ops = __kraus_operators(
+                    photon_loss_rates[loss_rate_index],
+                    time,
+                    cutoff,
+                    circuit.ops.get_a(cutoff),
+                    circuit.ops.get_N(cutoff),
+                )
                 operators = __tensor_operators(operators, kraus_ops)
                 kraus_tensor[unique_index] = True
                 loss_rate_index += 1
@@ -78,7 +87,6 @@ def __tensor_operators(current: list, new: list):
     return result
 
 
-
 def __kraus_operators(photon_loss_rate: float, time: float, cutoff: int, a, n):
     operators = []
     for photons in range(cutoff + 1):
@@ -86,9 +94,7 @@ def __kraus_operators(photon_loss_rate: float, time: float, cutoff: int, a, n):
             math.pow((1 - math.exp(-1 * photon_loss_rate * time)), photons)
             / math.factorial(photons)
         )
-        kraus = kraus * scipy.sparse.linalg.expm(
-            -1 * (photon_loss_rate / 2) * time * n
-        )
+        kraus = kraus * scipy.sparse.linalg.expm(-1 * (photon_loss_rate / 2) * time * n)
         kraus = kraus.dot(a**photons)
         operators.append(kraus.toarray())
 
@@ -105,11 +111,11 @@ def __init__(
         instructions: Sequence[str] = None,
         qumodes: Sequence[Qubit] = None,
         time_unit: str = "s",
-        dt: float = None
+        dt: float = None,
     ):
         """
         Initialize the Photon Loss noise pass
-        
+
         Args:
             photon_loss_rate (float): kappa, the rate of photon loss per second
             circuit (CVCircuit): cq2a.CVCircuit with ops for N and a, and cutoff
@@ -147,7 +153,7 @@ def __init__(
             if qmr_index not in qmr_to_num_qubits:
                 qmr_to_num_qubits[qmr_index] = 0
             qmr_to_num_qubits[qmr_index] += 1
-        
+
         self._num_qumodes = 0
         for qmr_index, num_qubits in qmr_to_num_qubits.items():
             num_qubits_per_qumode = circuit.get_qmr_num_qubits_per_qumode(qmr_index)
@@ -164,21 +170,27 @@ def __init__(
 
         # Test that we have the correct number of photon loss rates
         if len(self._photon_loss_rates) != self._num_qumodes:
-            raise Exception("List of photon loss rates must have same length as number of qumodes! (i.e., one rate per qumode)")
+            raise Exception(
+                "List of photon loss rates must have same length as number of qumodes! (i.e., one rate per qumode)"
+            )
 
         # Convert photon loss rate to photons per second
         if self._time_unit == "dt":
-            self.photon_loss_rates_sec = [rate * self._dt for rate in self._photon_loss_rates] 
+            self.photon_loss_rates_sec = [
+                rate * self._dt for rate in self._photon_loss_rates
+            ]
         else:
             conversion = 1.0 / apply_prefix(1.0, self._time_unit)
-            self.photon_loss_rates_sec = [rate * conversion for rate in self._photon_loss_rates] 
+            self.photon_loss_rates_sec = [
+                rate * conversion for rate in self._photon_loss_rates
+            ]
 
         super().__init__(self._photon_loss_error)
 
     def _photon_loss_error(self, op: Instruction, qubits: Sequence[int]):
         """Return photon loss error on each operand qubit"""
         error = None
-        
+
         if self.applies_to_instruction(op, qubits):
             if not op.duration:
                 if op.duration is None:
@@ -189,15 +201,21 @@ def _photon_loss_error(self, op: Instruction, qubits: Sequence[int]):
                     )
                 return None
 
-            # Qiskit `delay` gates are always for one qubit, see https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.delay.html            
+            # Qiskit `delay` gates are always for one qubit, see https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.delay.html
             if op.name.startswith("delay"):
-                warnings.warn("Qiskit applies delays as single qubit gates. Qumode PhotonLossNoisePass will not be applied")
+                warnings.warn(
+                    "Qiskit applies delays as single qubit gates. Qumode PhotonLossNoisePass will not be applied"
+                )
                 return None
 
             duration = self.duration_to_sec(op)
 
             kraus_operators = calculate_kraus(
-                self.photon_loss_rates_sec, duration, self._circuit, qubits, self._qumode_qubit_indices
+                self.photon_loss_rates_sec,
+                duration,
+                self._circuit,
+                qubits,
+                self._qumode_qubit_indices,
             )
 
             error = kraus_error(kraus_operators)
@@ -206,15 +224,20 @@ def _photon_loss_error(self, op: Instruction, qubits: Sequence[int]):
 
     def applies_to_instruction(self, op: Instruction, qubits: Sequence[int]):
         """Test if this PhotonLossNoisePass applies to the given instruction based on its name and qumodes (qubits)"""
-        return (self._instructions is None or op.name in self._instructions) and (self._qumode_qubit_indices is None or any(x in qubits for x in self._qumode_qubit_indices))
+        return (self._instructions is None or op.name in self._instructions) and (
+            self._qumode_qubit_indices is None
+            or any(x in qubits for x in self._qumode_qubit_indices)
+        )
 
     def duration_to_sec(self, op: Instruction):
         """Return the given Instruction's duration in seconds"""
         if op.unit == "dt":
             if self._dt is None:
-                raise NoiseError("PhotonLossNoisePass cannot apply noise to a 'dt' unit duration without a dt time set.")
+                raise NoiseError(
+                    "PhotonLossNoisePass cannot apply noise to a 'dt' unit duration without a dt time set."
+                )
             duration = op.duration * self._dt
         else:
             duration = apply_prefix(op.duration, op.unit)
 
-        return duration
\ No newline at end of file
+        return duration
diff --git a/c2qa/operators.py b/c2qa/operators.py
index 105f7ab..e5f2796 100644
--- a/c2qa/operators.py
+++ b/c2qa/operators.py
@@ -23,14 +23,14 @@ def get_a(self, cutoff: int):
         ).tocsc()
 
     def get_a1(self, cutoff_a: int, cutoff_b: int):
-            return scipy.sparse.kron(self.get_a(cutoff_a), self.get_eye(cutoff_b)).tocsc()
-    
+        return scipy.sparse.kron(self.get_a(cutoff_a), self.get_eye(cutoff_b)).tocsc()
+
     def get_a2(self, cutoff_a: int, cutoff_b: int):
         return scipy.sparse.kron(self.get_eye(cutoff_a), self.get_a(cutoff_b)).tocsc()
-        
+
     def get_a12(self, cutoff_a: int, cutoff_b: int):
         return self.get_a1(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b)
-    
+
     def get_a_dag(self, cutoff: int):
         """Creation operator"""
         a = self.get_a(cutoff)
@@ -41,19 +41,19 @@ def get_N(self, cutoff: int):
         a = self.get_a(cutoff)
         a_dag = self.get_a_dag(cutoff)
         return a_dag * a
-    
+
     def get_a1_dag(self, cutoff_a: int, cutoff_b: int):
         return self.get_a1(cutoff_a, cutoff_b).conjugate().transpose().tocsc()
-    
+
     def get_a2_dag(self, cutoff_a: int, cutoff_b: int):
         return self.get_a2(cutoff_a, cutoff_b).conjugate().transpose().tocsc()
-    
+
     def get_a12_dag(self, cutoff_a: int, cutoff_b: int):
         return self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a2_dag(cutoff_a, cutoff_b)
-    
+
     def get_a12dag(self, cutoff_a: int, cutoff_b: int):
         return self.get_a1(cutoff_a, cutoff_b) * self.get_a2_dag(cutoff_a, cutoff_b)
-    
+
     def get_a1dag2(self, cutoff_a: int, cutoff_b: int):
         return self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b)
 
@@ -83,7 +83,9 @@ def d(self, alpha, cutoff):
         Returns:
             csc_matrix: operator matrix
         """
-        arg = (alpha * self.get_a_dag(cutoff)) - (numpy.conjugate(alpha) * self.get_a(cutoff))
+        arg = (alpha * self.get_a_dag(cutoff)) - (
+            numpy.conjugate(alpha) * self.get_a(cutoff)
+        )
 
         return scipy.sparse.linalg.expm(arg)
 
@@ -114,7 +116,9 @@ def s2(self, theta, cutoff_a, cutoff_b):
             csc_matrix: operator matrix
         """
 
-        arg = (numpy.conjugate(theta * 1j) * self.get_a12_dag(cutoff_a, cutoff_b)) - (theta * 1j * self.get_a12(cutoff_a, cutoff_b))
+        arg = (numpy.conjugate(theta * 1j) * self.get_a12_dag(cutoff_a, cutoff_b)) - (
+            theta * 1j * self.get_a12(cutoff_a, cutoff_b)
+        )
 
         return scipy.sparse.linalg.expm(arg)
 
@@ -128,7 +132,9 @@ def bs(self, theta, cutoff_a, cutoff_b):
             csc_matrix: operator matrix
         """
 
-        arg = theta * self.get_a1dag2(cutoff_a, cutoff_b) - numpy.conj(theta) * self.get_a12dag(cutoff_a, cutoff_b)
+        arg = theta * self.get_a1dag2(cutoff_a, cutoff_b) - numpy.conj(
+            theta
+        ) * self.get_a12dag(cutoff_a, cutoff_b)
 
         return scipy.sparse.linalg.expm(arg)
 
@@ -181,10 +187,14 @@ def cd(self, theta, beta, cutoff):
         Returns:
             bsr_matrix: operator matrix
         """
-        displace0 = (theta * self.get_a_dag(cutoff)) - (numpy.conjugate(theta) * self.get_a(cutoff))
+        displace0 = (theta * self.get_a_dag(cutoff)) - (
+            numpy.conjugate(theta) * self.get_a(cutoff)
+        )
         if beta is None:
             beta = -theta
-        displace1 = (beta * self.get_a_dag(cutoff)) - (numpy.conjugate(beta) * self.get_a(cutoff))
+        displace1 = (beta * self.get_a_dag(cutoff)) - (
+            numpy.conjugate(beta) * self.get_a(cutoff)
+        )
 
         return scipy.sparse.kron(
             (idQB + zQB) / 2, scipy.sparse.linalg.expm(displace0)
@@ -199,7 +209,9 @@ def ecd(self, theta, cutoff):
         Returns:
             csr_matrix: operator matrix
         """
-        argm = (theta * self.get_a_dag(cutoff)) - (numpy.conjugate(theta) * self.get_a(cutoff))
+        argm = (theta * self.get_a_dag(cutoff)) - (
+            numpy.conjugate(theta) * self.get_a(cutoff)
+        )
         arg = scipy.sparse.kron(zQB, argm)
 
         return scipy.sparse.linalg.expm(arg)
@@ -213,8 +225,10 @@ def cbs(self, theta, cutoff_a, cutoff_b):
         Returns:
             csc_matrix: operator matrix
         """
-        
-        argm = theta * self.get_a1dag2(cutoff_a, cutoff_b) - numpy.conjugate(theta) * self.get_a12dag(cutoff_a, cutoff_b)
+
+        argm = theta * self.get_a1dag2(cutoff_a, cutoff_b) - numpy.conjugate(
+            theta
+        ) * self.get_a12dag(cutoff_a, cutoff_b)
         arg = scipy.sparse.kron(zQB, argm).tocsc()
 
         return scipy.sparse.linalg.expm(arg)
@@ -229,15 +243,32 @@ def cschwinger(self, beta, theta_1, phi_1, theta_2, phi_2, cutoff_a, cutoff_b):
             csc_matrix: operator matrix
         """
 
-        Sx = (self.get_a1(cutoff_a, cutoff_b) * self.get_a2_dag(cutoff_a, cutoff_b) + self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b))/2
-        Sy = (self.get_a1(cutoff_a, cutoff_b) * self.get_a2_dag(cutoff_a, cutoff_b) - self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b))/(2*1j)
-        Sz = (self.get_a2_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b) - self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a1(cutoff_a, cutoff_b))/2
-
-        sigma = numpy.sin(theta_1)*numpy.cos(phi_1)*xQB + numpy.sin(theta_1)*numpy.sin(phi_1)*yQB + numpy.cos(theta_1)*zQB
-        S = numpy.sin(theta_2)*numpy.cos(phi_2)*Sx + numpy.sin(theta_2)*numpy.sin(phi_2)*Sy + numpy.cos(theta_2)*Sz
+        Sx = (
+            self.get_a1(cutoff_a, cutoff_b) * self.get_a2_dag(cutoff_a, cutoff_b)
+            + self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b)
+        ) / 2
+        Sy = (
+            self.get_a1(cutoff_a, cutoff_b) * self.get_a2_dag(cutoff_a, cutoff_b)
+            - self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b)
+        ) / (2 * 1j)
+        Sz = (
+            self.get_a2_dag(cutoff_a, cutoff_b) * self.get_a2(cutoff_a, cutoff_b)
+            - self.get_a1_dag(cutoff_a, cutoff_b) * self.get_a1(cutoff_a, cutoff_b)
+        ) / 2
+
+        sigma = (
+            numpy.sin(theta_1) * numpy.cos(phi_1) * xQB
+            + numpy.sin(theta_1) * numpy.sin(phi_1) * yQB
+            + numpy.cos(theta_1) * zQB
+        )
+        S = (
+            numpy.sin(theta_2) * numpy.cos(phi_2) * Sx
+            + numpy.sin(theta_2) * numpy.sin(phi_2) * Sy
+            + numpy.cos(theta_2) * Sz
+        )
         arg = scipy.sparse.kron(sigma, S).tocsc()
 
-        return scipy.sparse.linalg.expm(-1j*beta*arg)
+        return scipy.sparse.linalg.expm(-1j * beta * arg)
 
     def snap(self, theta, n, cutoff):
         """SNAP (Selective Number-dependent Arbitrary Phase) operator
@@ -276,23 +307,23 @@ def csnap(self, theta, n, cutoff):
         # sparse_projector = scipy.sparse.csc_matrix(projector)
         arg = theta * 1j * scipy.sparse.kron(zQB, projector).tocsc()
         return scipy.sparse.linalg.expm(arg)
-        
+
     def multisnap(self, *args):
         """SNAP (Selective Number-dependent Arbitrary Phase) operator for multiple Fock states.
         Generates an arbitrary number of fock-number selective qubit rotations.
-        
+
         Args:
             args (List[reals, integers]): [List of phases, List of Fock states in which the mode should acquire the associated phase]
-        
+
         Returns:
             csr_matrix: operator matrix
         """
         # Divide list in two because thetas and ns must be sent in as a single list
         cutoff = args[-1]
         theta_ns = args[0:-1]
-        thetas = theta_ns[:len(theta_ns) // 2] # arguments
-        ns = theta_ns[len(theta_ns) // 2:] # Fock states on which they are applied
-        if len(thetas)!=len(ns): # one theta per Fock state to apply it to
+        thetas = theta_ns[: len(theta_ns) // 2]  # arguments
+        ns = theta_ns[len(theta_ns) // 2 :]  # Fock states on which they are applied
+        if len(thetas) != len(ns):  # one theta per Fock state to apply it to
             raise Exception("len(theta) must be equal to len(n)")
 
         id = numpy.eye(cutoff)
@@ -309,39 +340,38 @@ def multisnap(self, *args):
     def multicsnap(self, *args):
         """SNAP (Selective Number-dependent Arbitrary Phase) operator for multiple Fock states.
         Generates an arbitrary number of fock-number selective qubit rotations, with the qubit that accrues the geometric phase explicit.
-        
+
         Args:
             args (List[reals, integers]): [List of phases, List of Fock states in which the mode should acquire the associated phase]
-        
+
         Returns:
             csr_matrix: operator matrix
         """
         # Divide list in two because thetas and ns must be sent in as a single list
         cutoff = args[-1]
         theta_ns = args[0:-1]
-        thetas = theta_ns[:len(theta_ns) // 2] # arguments
-        ns = theta_ns[len(theta_ns) // 2:] # Fock states on which they are applied
-        if len(thetas)!=len(ns): # one theta per Fock state to apply it to
+        thetas = theta_ns[: len(theta_ns) // 2]  # arguments
+        ns = theta_ns[len(theta_ns) // 2 :]  # Fock states on which they are applied
+        if len(thetas) != len(ns):  # one theta per Fock state to apply it to
             raise Exception("len(theta) must be equal to len(n)")
 
         id = numpy.eye(cutoff)
-        gate = scipy.sparse.csr_matrix(scipy.sparse.kron(idQB,id))
+        gate = scipy.sparse.csr_matrix(scipy.sparse.kron(idQB, id))
         for i in range(len(ns)):
             ket_n = numpy.zeros(cutoff)
             ket_n[ns[i]] = 1
             projector = numpy.outer(ket_n, ket_n)
             coeff = scipy.sparse.linalg.expm(1j * thetas[i] * zQB) - idQB
-            mat = scipy.sparse.kron(coeff,projector).tocsr()
+            mat = scipy.sparse.kron(coeff, projector).tocsr()
             gate = numpy.add(gate, mat)
         return scipy.sparse.csr_matrix(gate)
 
-    
     def pnr(self, max, cutoff):
         """Support gate for photon number readout (see Curtis et al., PRA (2021) and Wang et al., PRX (2020))
-        
+
         Args:
             max (int): the period of the mapping
-        
+
         Returns:
             csc_matrix: operator matrix
         """
@@ -352,11 +382,11 @@ def pnr(self, max, cutoff):
             for i in range(j, cutoff, max):
                 ket_n = numpy.zeros(cutoff)
                 # fill from right to left
-                ket_n[-(i+1)] = 1
+                ket_n[-(i + 1)] = 1
                 projector += numpy.outer(ket_n, ket_n)
 
         # Flip qubit if there is a boson present in any of the modes addressed by the projector
-        arg = 1j * (-numpy.pi/2) * scipy.sparse.kron(xQB, projector).tocsc()
+        arg = 1j * (-numpy.pi / 2) * scipy.sparse.kron(xQB, projector).tocsc()
         return scipy.sparse.linalg.expm(arg)
 
     def eswap(self, theta, cutoff_a, cutoff_b):
@@ -379,7 +409,6 @@ def eswap(self, theta, cutoff_a, cutoff_b):
 
         return scipy.sparse.linalg.expm(arg)
 
-
     def csq(self, theta, cutoff):
         """Single-mode squeezing operator
 
@@ -391,11 +420,12 @@ def csq(self, theta, cutoff):
         """
         a_sqr = self.get_a(cutoff) * self.get_a(cutoff)
         a_dag_sqr = self.get_a_dag(cutoff) * self.get_a_dag(cutoff)
-        arg = scipy.sparse.kron(zQB, 0.5 * ((numpy.conjugate(theta) * a_sqr) - (theta * a_dag_sqr))).tocsc()
+        arg = scipy.sparse.kron(
+            zQB, 0.5 * ((numpy.conjugate(theta) * a_sqr) - (theta * a_dag_sqr))
+        ).tocsc()
 
         return scipy.sparse.linalg.expm(arg)
 
-
     def testqubitorderf(self, phi):
 
         arg = 1j * phi * scipy.sparse.kron(xQB, idQB)
@@ -403,10 +433,10 @@ def testqubitorderf(self, phi):
 
     def c_multiboson_sampling(self, max, cutoff):
         """SNAP gate creation for multiboson sampling purposes.
-        
+
         Args:
             max (int): the period of the mapping
-        
+
         Returns:
             dia_matrix: operator matrix
         """
@@ -419,8 +449,8 @@ def gate_from_matrix(self, matrix):
 
         Args:
             matrix (list): the (unitary) matrix that you wish to convert into a gate
-            
+
         Returns:
             csc_matrix: operator matrix
-        """ 
-        return scipy.sparse.csc_matrix(matrix)
\ No newline at end of file
+        """
+        return scipy.sparse.csc_matrix(matrix)
diff --git a/c2qa/parameterized_unitary_gate.py b/c2qa/parameterized_unitary_gate.py
index 03551ac..bd90183 100644
--- a/c2qa/parameterized_unitary_gate.py
+++ b/c2qa/parameterized_unitary_gate.py
@@ -12,7 +12,15 @@ class ParameterizedUnitaryGate(Gate):
     """UnitaryGate sublcass that stores the operator matrix for later reference by animation utility."""
 
     def __init__(
-        self, op_func, params, num_qubits, cutoffs, label=None, duration=100, unit="ns", discretized_param_indices: list = []
+        self,
+        op_func,
+        params,
+        num_qubits,
+        cutoffs,
+        label=None,
+        duration=100,
+        unit="ns",
+        discretized_param_indices: list = [],
     ):
         """Initialize ParameterizedUnitaryGate
 
@@ -56,7 +64,7 @@ def __array__(self, dtype=None):
                 )  # just cast everything to complex to avoid errors in Ubuntu/MacOS vs Windows
             else:
                 values.append(param)
-        
+
         # Add cutoff for each parameter
         values.extend(self.cutoffs)
 
@@ -103,7 +111,7 @@ def calculate_matrix(
         Args:
             current_step (int, optional): Current step within total_steps. Defaults to 1.
             total_steps (int, optional): Total steps to increment parameters. Defaults to 1.
-            
+
         Returns:
             ndarray: operator matrix
         """
@@ -131,12 +139,12 @@ def __calculate_segment_params(
 ):
     """
     Calculate the parameters at the current step. Return a tuples of the values.
-    
+
      Args:
         current_step (int): 0-based current step index of the discretization
         total_steps (int): total number of discretization steps
         keep_state (bool): true if the state should be kept between discretization steps (i.e., if the discretization value should be 1/total_steps vs current_step/total_steps)
-        
+
     Returns:
         discretized parameter values as tuple
     """
@@ -147,7 +155,11 @@ def __calculate_segment_params(
 
     values = []
     for index, param in enumerate(self.params):
-        if not hasattr(self, "discretized_param_indices") or len(self.discretized_param_indices) == 0 or index in self.discretized_param_indices:
+        if (
+            not hasattr(self, "discretized_param_indices")
+            or len(self.discretized_param_indices) == 0
+            or index in self.discretized_param_indices
+        ):
             values.append(param * param_fraction)
         else:
             values.append(param)
@@ -168,10 +180,14 @@ def __calculate_segment_duration(
             fraction = current_step / total_steps
 
         frame_duration = self.duration * fraction
-    
+
     return frame_duration, self.unit
 
 
 # Monkey patch Qiskit Instruction to support animating base Qiskit Instruction
-qiskit.circuit.instruction.Instruction.calculate_segment_params = __calculate_segment_params
-qiskit.circuit.instruction.Instruction.calculate_segment_duration = __calculate_segment_duration
+qiskit.circuit.instruction.Instruction.calculate_segment_params = (
+    __calculate_segment_params
+)
+qiskit.circuit.instruction.Instruction.calculate_segment_duration = (
+    __calculate_segment_duration
+)
diff --git a/c2qa/qumoderegister.py b/c2qa/qumoderegister.py
index 9e8aa2a..5a1ef9a 100644
--- a/c2qa/qumoderegister.py
+++ b/c2qa/qumoderegister.py
@@ -76,7 +76,7 @@ def __getitem__(self, key):
     def __len__(self):
         """The length of a QumodeRegister is the number of qumodes (not the num_qumodes * num_qubits_per_qumode)"""
         return self.num_qumodes
-    
+
     def __contains__(self, qubit):
         """Return true if this QumodeRegister contains the given qubit. This allows callers to use `in` python syntax."""
         return qubit in self.qreg
diff --git a/c2qa/util.py b/c2qa/util.py
index d0182c3..96235ca 100644
--- a/c2qa/util.py
+++ b/c2qa/util.py
@@ -15,10 +15,11 @@
 def flatten(l):
     return [item for sublist in l for item in sublist]
 
-def cv_ancilla_fock_measure(circuit,list_qumodes_to_sample:list, qmr_number:int=0):
+
+def cv_ancilla_fock_measure(circuit, list_qumodes_to_sample: list, qmr_number: int = 0):
     """Simulate a circuit with an appended binary search for boson number, and determine the Fock state of a set of qumodes using
     phase kickback on the qubit. For more information, see Curtis et al., PRA (2021) and Wang et al., PRX (2020).
-    
+
     Returns the Fock state of the qumodes in list_qumodes_to_sample, in qumode register qmr_number.
     """
     # Count number of qubits in circuit so far
@@ -26,40 +27,55 @@ def cv_ancilla_fock_measure(circuit,list_qumodes_to_sample:list, qmr_number:int=
     # Collect qumode register from circuit
     qmr = circuit.qmregs[qmr_number]
     # Add one ancilla qubits to the circuit per qumode to measure
-    qbr_extra = qiskit.QuantumRegister(size=len(list_qumodes_to_sample), name="qbr_sampling")
+    qbr_extra = qiskit.QuantumRegister(
+        size=len(list_qumodes_to_sample), name="qbr_sampling"
+    )
     # Add classical bits to readout measurement results
-    cbr_extra = qiskit.ClassicalRegister(len(list_qumodes_to_sample)*circuit.num_qubits_per_qumode, name="cbr_sampling")
-    circuit.add_register(qbr_extra,cbr_extra)
+    cbr_extra = qiskit.ClassicalRegister(
+        len(list_qumodes_to_sample) * circuit.num_qubits_per_qumode, name="cbr_sampling"
+    )
+    circuit.add_register(qbr_extra, cbr_extra)
     # Iterate over the qumodes
-    qumode_counter=0
+    qumode_counter = 0
     for j in list_qumodes_to_sample:
         # Set the initial maximum Fock state
         max = circuit.cutoff
         # Iterate a number of time corresponding to the number of bits required to represent the maximum Fock state in binary (remove useless characters at the front)
-        for iteration in range(len(bin(circuit.cutoff))-3):
+        for iteration in range(len(bin(circuit.cutoff)) - 3):
             # Make sure the ancilla qubit is always reset to 0
-            circuit.initialize('0',qbr_extra[qumode_counter])
+            circuit.initialize("0", qbr_extra[qumode_counter])
             # Apply a circuit which flips the ancilla if the qumode occupation is odd etc. see (Curtis et al., PRA, 2021 and Wang et al.,  PRX, 2020)
-            circuit.cv_c_pnr(max,qmr[list_qumodes_to_sample[j]],qbr_extra[qumode_counter])
+            circuit.cv_c_pnr(
+                max, qmr[list_qumodes_to_sample[j]], qbr_extra[qumode_counter]
+            )
             # Measure the qubit onto the classical bits (from left to right)
-            classical_bit=circuit.num_qubits_per_qumode-1-iteration+(qumode_counter*circuit.num_qubits_per_qumode)
-            circuit.measure(qbr_extra[qumode_counter],classical_bit)
+            classical_bit = (
+                circuit.num_qubits_per_qumode
+                - 1
+                - iteration
+                + (qumode_counter * circuit.num_qubits_per_qumode)
+            )
+            circuit.measure(qbr_extra[qumode_counter], classical_bit)
             # Update the maximum value for the SNAP gate creation
-            max = int(max/2)
+            max = int(max / 2)
         circuit.barrier()
-        qumode_counter+=1
+        qumode_counter += 1
     # Simulate circuit with a single shot
     _, result, _ = simulate(circuit, shots=1)
     # Return integer value of boson number occupation, converted from the bits which make up a binary number
     print(result.get_counts())
-    full_set_of_binary = list(result.get_counts().keys())[0].encode('ascii')
+    full_set_of_binary = list(result.get_counts().keys())[0].encode("ascii")
     results_integers = np.zeros([len(list_qumodes_to_sample)])
     for j in range(len(list_qumodes_to_sample)):
-        binary_number = full_set_of_binary[j*circuit.num_qubits_per_qumode:((j+1)*circuit.num_qubits_per_qumode)]
+        binary_number = full_set_of_binary[
+            j
+            * circuit.num_qubits_per_qumode : ((j + 1) * circuit.num_qubits_per_qumode)
+        ]
         print(binary_number)
-        results_integers[-(j+1)] = int(binary_number,2)
+        results_integers[-(j + 1)] = int(binary_number, 2)
     return results_integers
 
+
 def stateread(
     stateop, numberofqubits, numberofmodes, cutoff, verbose=True, little_endian=False
 ):
@@ -73,7 +89,9 @@ def stateread(
     amp_qb = []
     state = []
 
-    cutoff = 2**int(np.ceil(np.log2(cutoff))) # The cutoff needs to be a power of 2 for this code to work
+    cutoff = 2 ** int(
+        np.ceil(np.log2(cutoff))
+    )  # The cutoff needs to be a power of 2 for this code to work
 
     for i in range(len(st)):
         res = st[
@@ -92,7 +110,7 @@ def stateread(
             # first qubit because of how the kronecker product is made
             while iqb < numberofqubits:
                 # if the amplitude is in the first half of the state vector or remaining statevector
-                if (pos < sln / 2):
+                if pos < sln / 2:
                     qbst[iqb] = int(0)  # then the qubit is in 0
                 else:
                     # if the amplitude is in the second half then it is in 1
@@ -106,10 +124,10 @@ def stateread(
                     # print("pos (sln/2)", pos, "sln ",sln)
 
                 # only consider the part of the statevector corresponding to the qubit state which has just been discovered
-                sln = (sln / 2)
+                sln = sln / 2
 
                 # up the qubit counter to start finding out the state of the next qubit
-                iqb = (iqb + 1)
+                iqb = iqb + 1
             amp_qb.append((qbst * (np.abs(res) ** 2)).tolist())
 
             # Find the qumode states
@@ -127,7 +145,7 @@ def stateread(
                 # length of a division is the length of the statevector divided
                 # by the cutoff of the hilbert space (which corresponds to the
                 # number of fock states which a mode can have)
-                lendiv = (sln / cutoff)
+                lendiv = sln / cutoff
                 # print("lendiv (sln/cutoff)", lendiv)
                 val = pos / lendiv
                 # print("rough estimate of the position of the non-zero element: val (pos/lendiv) ", val)
@@ -188,7 +206,9 @@ def stateread(
     return [occupation_cv, occupation_qb], state
 
 
-def counts_to_fockcounts(circuit: CVCircuit, result: qiskit.result.result.Result, counts: dict = None):
+def counts_to_fockcounts(
+    circuit: CVCircuit, result: qiskit.result.result.Result, counts: dict = None
+):
     """Convert counts dictionary from Fock-basis binary representation into
     base-10 Fock basis (qubit measurements are left unchanged). Accepts the object returned by
     jobs.result(), along with the entire circuit.
@@ -211,13 +231,27 @@ def counts_to_fockcounts(circuit: CVCircuit, result: qiskit.result.result.Result
         max_iter_index = len(key) - 1
         newkey = key
 
-        # Using the nested list of qumode bit mappings, convert the relevant bits to base-10 integer and 
+        # Using the nested list of qumode bit mappings, convert the relevant bits to base-10 integer and
         # form new key by concatenating the unchanged bits of key around the decimal value.
         for index in range(len(key)):
             for qumode in qumode_bit_mapping:
                 if index == min(qumode):
-                    fock_decimal = str(int(key[max_iter_index - max(qumode) : max_iter_index - min(qumode) + 1], base=2))
-                    newkey = newkey[: max_iter_index - max(qumode)] + fock_decimal + newkey[max_iter_index - min(qumode) + 1: ]
+                    fock_decimal = str(
+                        int(
+                            key[
+                                max_iter_index
+                                - max(qumode) : max_iter_index
+                                - min(qumode)
+                                + 1
+                            ],
+                            base=2,
+                        )
+                    )
+                    newkey = (
+                        newkey[: max_iter_index - max(qumode)]
+                        + fock_decimal
+                        + newkey[max_iter_index - min(qumode) + 1 :]
+                    )
 
         newcounts[newkey] = counts[key]
 
@@ -233,17 +267,24 @@ def _final_qumode_mapping(circuit):
 
     # For each qumode qubit group, extract list of bits that map to qubits in group. Append list only if list is not empty
     for qumode_qubit_group in circuit.qumode_qubits_indices_grouped:
-        qumode_bit_group = [key for key, val in final_measurement_mapping.items() for qubit in qumode_qubit_group if val == qubit]
-        
+        qumode_bit_group = [
+            key
+            for key, val in final_measurement_mapping.items()
+            for qubit in qumode_qubit_group
+            if val == qubit
+        ]
+
         if qumode_bit_group != []:
             active_qumode_bit_indices_grouped.append(qumode_bit_group)
 
     # Sort nested list by first item in each sublist
-    active_qumode_bit_indices_grouped = sorted(active_qumode_bit_indices_grouped, key = lambda l: l[0])
+    active_qumode_bit_indices_grouped = sorted(
+        active_qumode_bit_indices_grouped, key=lambda l: l[0]
+    )
 
     return active_qumode_bit_indices_grouped
 
-    
+
 # This code is part of Mthree.
 #
 # (C) Copyright IBM 2021.
@@ -333,7 +374,11 @@ def measure_all_xyz(circuit: qiskit.QuantumCircuit):
     circuit_y.measure_all()  # Add measure gates in-place
     state_y, result_y, fockcounts_y = simulate(circuit_y)
 
-    return (state_x, result_x, fockcounts_x), (state_y, result_y, fockcounts_y), (state_z, result_z, fockcounts_z)
+    return (
+        (state_x, result_x, fockcounts_x),
+        (state_y, result_y, fockcounts_y),
+        (state_z, result_z, fockcounts_z),
+    )
 
 
 def get_probabilities(result: qiskit.result.Result):
@@ -393,7 +438,9 @@ def simulate(
     """
 
     if discretize and not noise_passes:
-        warnings.warn("Discretization of circuit intended for use with noise passes, but none provided")
+        warnings.warn(
+            "Discretization of circuit intended for use with noise passes, but none provided"
+        )
 
     if discretize:
         sim_circuit = discretize_circuits(cvcircuit)[-1]
@@ -422,7 +469,10 @@ def simulate(
 
     # Run and get statevector
     result = simulator.run(
-        circuit_compiled, shots=shots, max_parallel_threads=max_parallel_threads, noise_model=noise_model
+        circuit_compiled,
+        shots=shots,
+        max_parallel_threads=max_parallel_threads,
+        noise_model=noise_model,
     ).result()
 
     # The user may have added their own circuit.save_statevector
@@ -529,10 +579,10 @@ def trace_out_qubits(circuit: CVCircuit, state_vector):
 
 
 def cv_partial_trace(circuit: CVCircuit, state_vector, qubits: list):
-    """Return reduced density matrix over the given Qiskit Qubits. 
-    
+    """Return reduced density matrix over the given Qiskit Qubits.
+
     First find the indices of the given Qubits, then call qiskit.quantum_info.partial_trace
-    
+
     Args:
         circuit (CVCircuit): circuit with results to trace (to find Qubit index)
         state_vector (Statevector or DensityMatrix): simulation results to trace over
@@ -540,22 +590,23 @@ def cv_partial_trace(circuit: CVCircuit, state_vector, qubits: list):
 
     Returns:
         DensityMatrix: partial trace"""
-    
+
     if not isinstance(qubits, list):
         qubits = [qubits]
     indices = circuit.get_qubit_indices(qubits)
 
     return qiskit.quantum_info.partial_trace(state_vector, indices)
 
+
 def fockmap(matrix, fock_input, fock_output, amplitude=[]):
     """Generates matrix corresponding to some specified mapping of Fock states for a single qumode.
     First feed function empty matrix, then call fmap_matrix however many times needed to fully define intended mapping.
     Maps ith element in fock_input to ith element in fock_output with amplitude specified by ith element in amplitude.
     If amplitude is left blank, function assumes amp = 1 for all mappings.
 
-    Two use cases 
-    1) int + list datatype combination (length of amp list must match length of either fock_input or fock_output, whichever is longer): 
-    >fockmap(matrix, 1, [0, 1]) 
+    Two use cases
+    1) int + list datatype combination (length of amp list must match length of either fock_input or fock_output, whichever is longer):
+    >fockmap(matrix, 1, [0, 1])
     ->> ``|0><1| + |1><1|``
 
     >fockmap(matrix, [3, 2], 0, [0.5j, 1])
@@ -577,19 +628,18 @@ def fockmap(matrix, fock_input, fock_output, amplitude=[]):
 
     Returns:
         np.array: Edited matrix"""
-    
 
     # Convert args to lists for convenience of computation
     if isinstance(fock_input, int):
         fock_input = [fock_input]
     elif isinstance(fock_input, (float, complex, str, bool)):
         raise TypeError("Please ensure that your fock_input value is an int")
-    
+
     if isinstance(fock_output, int):
         fock_output = [fock_output]
     elif isinstance(fock_output, (float, complex, str, bool)):
         raise TypeError("Please ensure that your fock_output value is an int")
-    
+
     if isinstance(amplitude, (int, float, complex)):
         amplitude = [amplitude]
     elif isinstance(amplitude, np.ndarray):
@@ -599,31 +649,45 @@ def fockmap(matrix, fock_input, fock_output, amplitude=[]):
     matrix = np.array(matrix, dtype=complex)
 
     # Default amplitude is 1 for all states, unless otherwise specified
-    if not amplitude: # Empty lists evaluate as False
+    if not amplitude:  # Empty lists evaluate as False
         amplitude = [1 for i in range(max(len(fock_input), len(fock_output)))]
 
     ## Error flags
     # Length of amplitude list must match the greater of either input or output
     if not (len(amplitude) == max(len(fock_input), len(fock_output))):
-            raise ValueError("Please ensure that that length of amplitude arg matches length of either input or output list.")
+        raise ValueError(
+            "Please ensure that that length of amplitude arg matches length of either input or output list."
+        )
     # Datatype of args check
-    if not (isinstance(fock_input, list) & isinstance(fock_output, list) & isinstance(amplitude, (list, np.ndarray))):
-        raise TypeError("Please ensure that datatypes of input and output states are either int or list.")
+    if not (
+        isinstance(fock_input, list)
+        & isinstance(fock_output, list)
+        & isinstance(amplitude, (list, np.ndarray))
+    ):
+        raise TypeError(
+            "Please ensure that datatypes of input and output states are either int or list."
+        )
     # Check for cutoff
     n, m = matrix.shape
     if n != m:
         raise ValueError("Matrix given is not square")
-    if any(i >= n for i in fock_input) or any(j >= n for j in fock_output): 
+    if any(i >= n for i in fock_input) or any(j >= n for j in fock_output):
         raise ValueError("Fock state(s) greater than cutoff.")
 
     ## Use cases
     # 1. Int + int datatype for input/output args.
-    if ((len(fock_input) == 1) & (len(fock_output) == 1)):
+    if (len(fock_input) == 1) & (len(fock_output) == 1):
         if len(amplitude) > 1:
-            raise ValueError("Please ensure that only a single amplitude value is provided, as there is only 1 mapping provided")
+            raise ValueError(
+                "Please ensure that only a single amplitude value is provided, as there is only 1 mapping provided"
+            )
         if matrix[fock_output[0], fock_input[0]] != 0:
-            print("Warning: Existing element for |{}><{}| will be overwritten".format(fock_output[0], fock_input[0]))
-                
+            print(
+                "Warning: Existing element for |{}><{}| will be overwritten".format(
+                    fock_output[0], fock_input[0]
+                )
+            )
+
         matrix[fock_output[0], fock_input[0]] = amplitude[0]
 
         return matrix
@@ -632,37 +696,49 @@ def fockmap(matrix, fock_input, fock_output, amplitude=[]):
     elif (len(fock_input) == 1) & (len(fock_output) > 1):
         for i in range(len(fock_output)):
             if matrix[fock_output[i], fock_input[0]] != 0:
-                print("Warning: Existing element for |{}><{}| will be overwritten".format(fock_output[i], fock_input[0]))
+                print(
+                    "Warning: Existing element for |{}><{}| will be overwritten".format(
+                        fock_output[i], fock_input[0]
+                    )
+                )
 
             matrix[fock_output[i], fock_input[0]] = amplitude[i]
 
         return matrix
-    
+
     elif (len(fock_output) == 1) & (len(fock_input) > 1):
         for i in range(len(fock_input)):
             if matrix[fock_output[0], fock_input[i]] != 0:
-                print("Warning: Existing element for |{}><{}| will be overwritten".format(fock_output[0], fock_input[i]))
+                print(
+                    "Warning: Existing element for |{}><{}| will be overwritten".format(
+                        fock_output[0], fock_input[i]
+                    )
+                )
 
             matrix[fock_output[0], fock_input[i]] = amplitude[i]
 
-        return matrix        
+        return matrix
 
     # 3. List datatype input/output/amp args. Lengths of all must match
-    elif (len(fock_input) == len(fock_output) == len(amplitude)):
+    elif len(fock_input) == len(fock_output) == len(amplitude):
 
         for i in range(len(fock_input)):
             if matrix[fock_output[i], fock_input[i]] != 0:
-                print("Warning: Existing element for |{}><{}| will be overwritten".format(fock_output[i], fock_input[i]))
-                
+                print(
+                    "Warning: Existing element for |{}><{}| will be overwritten".format(
+                        fock_output[i], fock_input[i]
+                    )
+                )
+
             matrix[fock_output[i], fock_input[i]] = amplitude[i]
 
         return matrix
-    
+
     else:
         raise ValueError("Please ensure that your args are correctly defined.")
-    
 
-def avg_photon_num(circuit: CVCircuit, state, decimals: int=2):
+
+def avg_photon_num(circuit: CVCircuit, state, decimals: int = 2):
     """Returns average photon number of state for each qumode within the circuit using the number operator.
 
     Args:
@@ -683,10 +759,11 @@ def avg_photon_num(circuit: CVCircuit, state, decimals: int=2):
                 traced_qubits.extend(qumode_qubits[traced_qumode])
         traced_state = qiskit.quantum_info.partial_trace(state, traced_qubits)
         averages.append(qumode_avg_photon_num(traced_state, decimals))
-    
+
     return averages
 
-def qumode_avg_photon_num(state, decimals: int=2):
+
+def qumode_avg_photon_num(state, decimals: int = 2):
     """Returns average photon number of an individual qumode's state using the number operator.
 
     Args:
@@ -700,19 +777,25 @@ def qumode_avg_photon_num(state, decimals: int=2):
     # Generate number operator based on dimension of state
     dim = state.dim
     N = np.diag(range(dim))
-    
+
     # Normalise state
     if isinstance(state, Statevector):
         for_norm = state.inner(state)
     elif isinstance(state, DensityMatrix):
-        for_norm = state.trace()  
+        for_norm = state.trace()
     else:
-        raise TypeError("Only Statevector or DensityMatrix are accepted as valid types.")
-    
+        raise TypeError(
+            "Only Statevector or DensityMatrix are accepted as valid types."
+        )
+
     # Calculate average photon number
-    avg_photon = state.expectation_value(N)/for_norm
-    
+    avg_photon = state.expectation_value(N) / for_norm
+
     if round(avg_photon.imag, 6) != 0:
-        raise Exception("Magnitude of average photon is complex, check inputs. Imaginary portion = {}".format(avg_photon.imag))
-    
+        raise Exception(
+            "Magnitude of average photon is complex, check inputs. Imaginary portion = {}".format(
+                avg_photon.imag
+            )
+        )
+
     return np.round(avg_photon.real, decimals)
diff --git a/c2qa/wigner.py b/c2qa/wigner.py
index 61106c0..47ede3d 100644
--- a/c2qa/wigner.py
+++ b/c2qa/wigner.py
@@ -20,18 +20,18 @@ def simulate_wigner(
     circuit: CVCircuit,
     xvec: np.ndarray,
     shots: int,
-    noise_passes = None,
+    noise_passes=None,
     conditional: bool = True,
     trace: bool = False,
-    g = sqrt(2),
-    method: str = "clenshaw"
+    g=sqrt(2),
+    method: str = "clenshaw",
 ):
     """Simulate the circuit, optionally partial trace the results, and calculate the Wigner function."""
     states, _, _ = simulate(
         circuit,
         shots=shots,
         noise_passes=noise_passes,
-        conditional_state_vector=conditional
+        conditional_state_vector=conditional,
     )
 
     if states:
@@ -62,18 +62,14 @@ def simulate_wigner_multiple_statevectors(
     xvec: np.ndarray,
     shots: int,
     statevector_label: str,
-    num_statevectors:int,
+    num_statevectors: int,
     noise_passes=None,
     trace: bool = False,
-    g = sqrt(2),
-    method: str = "clenshaw"
+    g=sqrt(2),
+    method: str = "clenshaw",
 ):
     """Simulate the circuit, optionally partial trace the results, and calculate the Wigner function on each statevector starting with the given label."""
-    state, result, _ = simulate(
-        circuit,
-        shots=shots,
-        noise_passes=noise_passes
-    )
+    state, result, _ = simulate(circuit, shots=shots, noise_passes=noise_passes)
 
     if len(result.results):
         wigner_results = []
@@ -93,13 +89,14 @@ def simulate_wigner_multiple_statevectors(
 
     return wigner_results
 
+
 def wigner(
     state,
     axes_min: int = -6,
     axes_max: int = 6,
     axes_steps: int = 200,
-    g = sqrt(2),
-    method: str = "clenshaw"
+    g=sqrt(2),
+    method: str = "clenshaw",
 ):
     """
     Calculate the Wigner function on the given state vector.
@@ -124,8 +121,8 @@ def wigner_mle(
     axes_min: int = -6,
     axes_max: int = 6,
     axes_steps: int = 200,
-    g = sqrt(2),
-    method: str ="clenshaw"
+    g=sqrt(2),
+    method: str = "clenshaw",
 ):
     """
     Find the maximum likelihood estimation for the given state vectors and calculate the Wigner function on the result.
@@ -157,7 +154,7 @@ def wigner_mle(
     return wigner(mle_normalized, axes_min, axes_max, axes_steps, g=g, method=method)
 
 
-def _wigner(state, xvec, yvec = None, g = sqrt(2), method: str = "clenshaw"):
+def _wigner(state, xvec, yvec=None, g=sqrt(2), method: str = "clenshaw"):
     if isinstance(state, DensityMatrix):
         rho = state.data
     else:
@@ -180,8 +177,8 @@ def plot_wigner(
     num_colors: int = 100,
     draw_grid: bool = False,
     dpi: int = 100,
-    g = sqrt(2),
-    method: str = "clenshaw"
+    g=sqrt(2),
+    method: str = "clenshaw",
 ):
     """Produce a Matplotlib figure for the Wigner function on the given state vector.
 
@@ -205,11 +202,11 @@ def plot_wigner(
 
     w_fock = wigner(
         state=state,
-        axes_min=axes_min, 
+        axes_min=axes_min,
         axes_max=axes_max,
         axes_steps=axes_steps,
         g=g,
-        method=method
+        method=method,
     )
 
     plot(
@@ -220,7 +217,7 @@ def plot_wigner(
         file=file,
         num_colors=num_colors,
         draw_grid=draw_grid,
-        dpi = dpi
+        dpi=dpi,
     )
 
 
@@ -232,7 +229,7 @@ def plot(
     file: str = None,
     num_colors: int = 100,
     draw_grid: bool = False,
-    dpi = 100
+    dpi=100,
 ):
     """Contour plot the given data array"""
     xvec = np.linspace(axes_min, axes_max, axes_steps)
@@ -254,13 +251,12 @@ def plot(
     # ax.set_xticks(xvec_int)
     ax.set_ylabel(r"$p$")
     # ax.set_yticks(xvec_int)
-    ax.set_aspect('equal', 'box')
+    ax.set_aspect("equal", "box")
     if draw_grid:
         ax.grid()
 
-
-    cb = fig.colorbar(cont, ax=ax, format=tick.FormatStrFormatter('%.2f'))
-    cb.set_label(r"$W(x,p)$",rotation=270,labelpad=25)
+    cb = fig.colorbar(cont, ax=ax, format=tick.FormatStrFormatter("%.2f"))
+    cb.set_label(r"$W(x,p)$", rotation=270, labelpad=25)
 
     if file:
         plt.savefig(file, dpi=dpi)
@@ -268,7 +264,14 @@ def plot(
         plt.show()
 
 
-def plot_wigner_projection(circuit: CVCircuit, qubit, file: str = None, draw_grid: bool = False, g = sqrt(2), method: str = "clenshaw"):
+def plot_wigner_projection(
+    circuit: CVCircuit,
+    qubit,
+    file: str = None,
+    draw_grid: bool = False,
+    g=sqrt(2),
+    method: str = "clenshaw",
+):
     """Plot the projection onto 0, 1, +, - for the given circuit.
 
     This is limited to CVCircuit with only one qubit, also provided as a parameter.
@@ -338,7 +341,9 @@ def plot_wigner_projection(circuit: CVCircuit, qubit, file: str = None, draw_gri
     _add_contourf(ax0, fig, "Projection onto zero", xvec, xvec, wigner_zero, draw_grid)
     _add_contourf(ax1, fig, "Projection onto one", xvec, xvec, wigner_one, draw_grid)
     _add_contourf(ax2, fig, "Projection onto plus", xvec, xvec, wigner_plus, draw_grid)
-    _add_contourf(ax3, fig, "Projection onto minus", xvec, xvec, wigner_minus, draw_grid)
+    _add_contourf(
+        ax3, fig, "Projection onto minus", xvec, xvec, wigner_minus, draw_grid
+    )
 
     # Save to file or display
     if file:
@@ -349,15 +354,15 @@ def plot_wigner_projection(circuit: CVCircuit, qubit, file: str = None, draw_gri
 
 def plot_wigner_snapshot(
     circuit: CVCircuit,
-    result: Result, 
+    result: Result,
     folder: Path = None,
     trace: bool = True,
     axes_min: int = -6,
     axes_max: int = 6,
     axes_steps: int = 200,
     num_colors: int = 100,
-    g = sqrt(2),
-    method: str = "clenshaw"
+    g=sqrt(2),
+    method: str = "clenshaw",
 ):
     for cv_snapshot_id in range(circuit.cv_snapshot_id):
         label = f"cv_snapshot_{cv_snapshot_id}"
@@ -366,7 +371,7 @@ def plot_wigner_snapshot(
             file = Path(folder, f"{label}.png")
         else:
             file = f"{label}.png"
-        
+
         snapshot = result.data()[label]
         # index = 0
         # if len(snapshot) > 1:
@@ -374,7 +379,18 @@ def plot_wigner_snapshot(
         #     index = len(snapshot) - 1
 
         # plot_wigner(circuit, snapshot[index], trace, file, axes_min, axes_max, axes_steps, num_colors, g=g, method=method)
-        plot_wigner(circuit, snapshot, trace, file, axes_min, axes_max, axes_steps, num_colors, g=g, method=method)
+        plot_wigner(
+            circuit,
+            snapshot,
+            trace,
+            file,
+            axes_min,
+            axes_max,
+            axes_steps,
+            num_colors,
+            g=g,
+            method=method,
+        )
 
 
 def _add_contourf(ax, fig, title, x, y, z, draw_grid: bool = False):
diff --git a/pyproject.toml b/pyproject.toml
index ee5c066..e901186 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,12 +13,13 @@ classifiers = [
     "Operating System :: OS Independent",
 ]
 dependencies = [
-    "qiskit==1.0.1",
-    "qiskit_aer==0.13.3",
-    "qiskit-ibm-runtime==0.20.0",
-    "qutip==5.0.0",
-    "matplotlib==3.7.3",
-    "pylatexenc==2.10"
+    "qiskit==1.2.4",
+    "qiskit_aer==0.15.1",
+    "qiskit-ibm-runtime==0.31.0",
+    "qutip==5.0.4",
+    "matplotlib==3.9.2",
+    "pylatexenc==2.10",
+    "numpy<2.0.0"
 ]
 
 [project.urls]
diff --git a/requirements.txt b/requirements.txt
index fb1f821..ee77d1e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,11 +1,15 @@
-qiskit==1.0.1
-qiskit-aer==0.13.3
-qiskit-ibm-runtime==0.20.0
-qutip==5.0.0
+qiskit==1.2.4
+qiskit-aer==0.15.1
+qiskit-ibm-runtime==0.31.0
+qutip==5.0.4
 
 # QuTiP install failing without setuptools
 setuptools
 
 # For drawing circuits, state vectors, Wigner function plots
-matplotlib==3.7.3
+matplotlib==3.9.2
 pylatexenc==2.10
+
+# tutorials/medium_artical/examples.ipynb not compatible with NumPy >2.0.0
+#   It causes type & casting validation errors in Qiskit Gate
+numpy<2.0.0
\ No newline at end of file
diff --git a/tutorials/medium_article/examples.ipynb b/tutorials/medium_article/examples.ipynb
index 7d9cc69..2f9a298 100644
--- a/tutorials/medium_article/examples.ipynb
+++ b/tutorials/medium_article/examples.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "id": "f54f2757-b5fa-4650-814c-9f75a7dfc9fe",
    "metadata": {
     "execution": {
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "id": "a2817334-6a69-4932-89b4-7e3cbe6b2711",
    "metadata": {
     "execution": {
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 25,
    "id": "7ff1ee5d-a89a-4ea3-a1f2-7213b5c127c3",
    "metadata": {
     "execution": {
@@ -78,10 +78,10 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x133afc41060>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x2150ae0bee0>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 26,
    "id": "5c246389-3c89-4e36-bf60-3a8854bec14d",
    "metadata": {
     "execution": {
@@ -129,7 +129,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Ftm3bEI1GMX78+Ky3WWoY4IiIiKgo/H4/pk2bhvb2dmlZIpFAe3s7Zs2alfP2Bw4ciBEjRuCzzz7Dn/70J3zjG9/IeZulgodQiYiIqGiampqwZMkSTJ8+HTNnzkRrayt6enqwbNkyAMBFF12EUaNGSePoIpEI3n77ben/+/btw86dOzFo0CBMmDABAPCnP/0JgiDgi1/8Inbv3o2rrroKkyZNkrbZHzDAERERlTG/1w2f154Dcq4strN48WIcOHAAa9euRWdnJ+rq6tDW1iad2LBnzx643X3b/fjjjzFlyhTp51tvvRW33norTj/9dDz//PMAgO7ubqxZswb/+te/MGTIECxatAg/+9nP4PP5cnuBJcQlCIJQ7J0opFAohOrqanR2dSkGWBIRERVLKBRCbU0Nuru7C9Y3if3h/739f+GrGGjLNqNHe/D0DxsK+jrKFcfAERERETkMAxwRERGRw3AMHBERURkb4HfD58/uIvRq0TjrQoXCd5qIiIjIYRjgiIiIiByGAY6IiIjIYTgGjoiIqIz5PB74vfaMgYPHpu1QRqzAERERETkMAxwRERGRwzDAERERETkMx8ARERGVsQq/B36b5oHzxDgGrlBYgSMiIiJyGAY4IiIiIodhgCMiIiJyGAY4IiIiIofhSQxERERlzOdxw++xp54j2LQdyozvNBEREZHDMMAREREROQwDHBEREZHDcAwcERFRGRvg9yDAiXwdhxU4IiIiIodhgCMiIiJyGAY4IiIiIofhGDgiIqIyFvR5ELRpDJwryjFwhcIKHBEREZHDMMAREREROQwDHBEREZHDcAwcERFRGfN73fB77annJGzaDmXGd5qIiIjIYRjgiIiIiByGAY6IiIjIYRjgiIiIiByGAY6IiKiMVfjdqPB7bLplFys2bNiAsWPHIhgMor6+Htu2bdNd96233sKiRYswduxYuFwutLa2pq3T0tKCGTNmoLKyEsOHD8fChQuxa9eurPatVDHAERERUdFs2bIFTU1NaG5uxo4dOzB58mQ0NjZi//79musfOXIE48ePx0033YTa2lrNdV544QWsWLECL7/8Mp599llEo1HMnz8fPT09+XwpBcVpRIiIiKhobrvtNixfvhzLli0DAGzcuBFPPfUU7r33XqxevTpt/RkzZmDGjBkAoHk/ALS1tSl+3rx5M4YPH47t27fjtNNOs/kVFAcrcERERGSrUCikuIXDYc31IpEItm/fjoaGBmmZ2+1GQ0MDOjo6bNuf7u5uAMCQIUNs22axMcARERGVMZ/HDb9NN58nGStGjx6N6upq6dbS0qL53AcPHkQ8HkdNTY1ieU1NDTo7O215fYlEAqtWrcKpp56Kk08+2ZZtlgIeQiUiIiJb7d27F1VVVdLPgUCgaPuyYsUKvPnmm3jppZeKtg/5wABHREREtqqqqlIEOD1Dhw6Fx+NBV1eXYnlXV5fuCQpWrFy5Ek8++SRefPFFHHfccTlvr5TwECoREREVhd/vx7Rp09De3i4tSyQSaG9vx6xZs7LeriAIWLlyJR5//HE899xzGDdunB27W1JYgSMiIipjAY8bAbsuZu+xvp2mpiYsWbIE06dPx8yZM9Ha2oqenh7prNSLLroIo0aNksbRRSIRvP3229L/9+3bh507d2LQoEGYMGECgORh04cffhi///3vUVlZKY2nq66uRkVFhR0vtegY4IiIiKhoFi9ejAMHDmDt2rXo7OxEXV0d2trapBMb9uzZA7e7Lxh+/PHHmDJlivTzrbfeiltvvRWnn346nn/+eQDAXXfdBQCYO3eu4rnuu+8+LF26NK+vp1AY4IiIiKioVq5ciZUrV2reJ4Yy0dixYyEIguH2Mt3fH3AMHBEREZHDsAJHRJrc8aht20p4fLZti4iIHFaBK4eL0xIVgzseTbs5aftElL2Ax42gTbdAFicxUHYc9U6Xw8VpiQql2GGKQY6IKHuOOoRaDhenLRfxRPoAU4/bVYQ9KU+lFJzc8SgPsRaYuv2x7RE5j6MCnJqZi9OGw2HFRXRDoVDe94vSaQU2M+uwYyHKnpl2p7ce2x5RaXNsgDN7cdqWlhZcd911BdwzEpntPKxsg52KPRIeX8lU4Vh9s48dbU69Lba5/s/rccNr09g1u7ZDmTn2nRYvTvvII48YrrdmzRp0d3dLt7179xZoD8tXPCHY2pFobVt+o+wkPD7pVk7P3V8Uqi2wnRGVJkdW4KxcnDYQCCAQCBRoz8pbsf7I6z0vKwfmyYNUvipzDGvZK4UAxYocUWlxVIATBAGXXXYZHn/8cTz//PP98uK0TlQKnYsWjuvJTqagpRfwGNByV6ptSY5Bjqg0OCrAlcPFaZ3ECZ2NGsfU5Y5BzT5ObEMiBrn+I+h1I2jXxext2g5l5qh3+q677kJ3dzfmzp2LESNGSLctW7YUe9fKjpM7HjmOpaNi6E+/c/3ptRA5iaMqcOVwcdpS15//ULOiQPnUn9sOwPZDVGiOCnBUPP2985FzckfU3z8nfialz8nth8hJGODIULl1PnKl3BGV6+fipDOOy/UzEsUTQkl+LpTO53bBZ9NnZdd2KDMGONJU7p2PnPy9KEaHlK/PIlYin7HXhve0lM44ZtvpU8pfgoicjgGOFArZ+VgJEHZ08nbId4dk9/tfKiHNiNE+5vK5FzLUlVpoK7W2xSBHZD8GOAKQ/w4o1yCRr04+W7lOR1JqVbV4gfOHx+TbpX49uX7Wdh6CLYXQZkdA19pGvtoUgxyRfRjgylw+O6FCVX8K2QHpKWZnbvV9LnRYs7IPmYKd3YFO2p8SCGOZFLKamq/3WcQgR5Q7Brgylo9Oq1QO2cn3o1QOv9rJyvtsJbBF8/z5ZRrgLN9XM1U68X0o98843/L1PjPIlQa/x42ATRPwxngx+4JhgCtD/Tm4aelPYS4fwS3foc3ouYwCnZUw11+CXCm3IyB/bYlBjsg6BrgyUm7BTYtTw5zdwc1KaIsnTK+qYOaLuHw/zIS5/hjknNaGRPl4rzn1CJF5DHBlohTPbsx2LJbZAfCZOLGzz6RUwpv8scU4ohJLCCX/udod3HIZ25hLm7K7HbEaR2QOA1w/VyrBzc6B81rbKqUOyG7FqtDkEt7U2zFbjTMzRs6uAF8sxfzyY3Z72bzH+QhyDHGF4XO74XPb803Lru1QZgxw/Zid4S2bTqeQZzuWYgdEJJdLcCv0mcO5tCc72xGrcUT6GOD6oWIGt1KYogKwfjajXKkFOa/bZfpz8LgyfwZilSvToVR11cxsRS6bQ6ZmLr/j1MpbKVStc2V2DKKcnYexWY0jSscA18/YFd4KFdysngGZzXX2sg1zpTSOymqIA+wLctJ2bT4yYvaztBrcSuUzAwr/BSjf7amY07wwxBEpMcD1I8UIb4WeY0xvG2Y7IquVhFKqxqn3IdPnpPUatT4vK514ps8w1wtZZ1tlK4XPRy1f7UiUr/aUj7ZkVztiiMuPgNeFoF3zwHn5+RQKA1w/YUd4c/IcY1bmFwOyC3KlFhL09sfoc7QakNSfczYBza5Dn6X2/hsx25ZKaZJlveexsy3Z0Y4Y4oiSGOD6gUKGN7unqUjbvsY4q2wO3Yn7UGqdTyHYuY96fyBK4fJlpapU2hJgT3uyuy3ZUY1jiCNigHO8XMNbMTobq9NTGK2fqTOyMlFsoUJcf+h4+sNr0FKI9lTMtpTpMUbtqdTaEkMclTsGOAcrhfBmpqOxaz4xM9s20wHpdT7ZnGlnVr46Gnc8mpftOknC47NtW/LPyWr7siO8ZWpP+WxL6u0Xoi0xxJWGgNe+a6FGbdoOZcZ32qEKVSnINrzFE303s/uT6WaGmeeNJgTjfc/wVFbGCnrcrqw6GHc8auqm3PGo6ZsrerTkblb23+p7lQ27g0GmtqT3O5mPtmSq/RegLcn3N1v5uEQgFd6GDRswduxYBINB1NfXY9u2bYbrP/roo5g0aRKCwSC+/OUv4+mnn05b55///CfOPvtsVFdXY+DAgZgxYwb27NmTr5dQcKzAlaFcKwWZgluuz231sXrf4DNdyslo5n87Zvy3EgBMhYwM67gSMf07Y5GMmzd8vM0Ed/qfHhdk++j1Gz/W6L3QqMhpvb9mKncet8tUQDD6vS7FtqT3+ExtCdBuT0YVuUJU41iJc7YtW7agqakJGzduRH19PVpbW9HY2Ihdu3Zh+PDhaev//e9/x/nnn4+Wlhb8x3/8Bx5++GEsXLgQO3bswMknnwwAeP/99zFnzhx897vfxXXXXYeqqiq89dZbCAaDhX55eeMSBKGsvr6EQiFUV1ejs6sLVVVVxd6drOTyjTPTH/18dDaFvBRUpg5AL8zpBTmjTsfoucx0JhlDm879ukFLI6TprevSe+545qCXE49BMNMIVFpBTy/caa4rPa9xWMsU5jK1Ob3f8WzaU6m0JSDT77j2cqPxcWa+FOVySNXJIS4UCqG2pgbd3d0F65vE/vBv7+zBoEp7nvPzwyGcOul4S6+jvr4eM2bMwJ133gkASCQSGD16NC677DKsXr06bf3Fixejp6cHTz75pLTsK1/5Curq6rBx40YAwHnnnQefz4cHH3zQhldVmliBc5hSC296nU2m57JjSgStjkL+vFodgd51OfWqcfm49qZhcNO4TzOEmQhraSFNI5y54unb1g13ORI8PkD1fIKn70+QS9w/WchzxaNpwU7QyqRef9rrVwQ6+WvSqdAZhTijSpzVUGV3WzLapll6ocvojFG9Cnemaly+x8VRaQiFQoqfA4EAAoFA2nqRSATbt2/HmjVrpGVutxsNDQ3o6OjQ3HZHRweampoUyxobG/HEE08ASAbAp556CldffTUaGxvx2muvYdy4cVizZg0WLlyY2wsrIQxwDlKM8GZnZ2P3PFaZ5qvS63yMOh47QpxRFUA3vJkJbqrQZiWwqYNa2rrq8BPp1d7PLLn8wfTX4/Yq9kMKavFYerAzqty5vcr3JlWhkz+fZphTBTbxs7HrpAitNmWlPZViWwK025PVL0WAcZvKNsTxUGp2PC4XPC573jdxO6NHj1Ysb25uxrp169LWP3jwIOLxOGpqahTLa2pq8M4772g+R2dnp+b6nZ2dAID9+/fj888/x0033YQbbrgBN998M9ra2vDNb34Tf/nLX3D66adn+/JKCgNcGbAzvFkNbhnPqsviCL7eHxq9b/5GQc5siNNitZPRDG85BjdFENMJbIp15KFGFdKEsOpnE+PmzHB5/YptuwLKMSguf/JnVyIGpIKWuvIm/y0xesczhbm0IJdFNc4Ms+HNSnDLR1sCtNuT0ZQhWu0pH9U4hjhn27t3r+IQqlb1LV8SieQv5De+8Q1cccUVAIC6ujr8/e9/x8aNGxngqLCyrb7lO7xpbd/4rLTcKwfqbag7IL3Ox2zHoxXicj2UmlV4syu46YQ2eahShzWtCpwQNX9o1eWTha/UtsSgJsQicMnGsQnhXinUiesA+kFN+nRTVTl52EsLarGIYsycKxEzFeLM0vr9zza8WQ1udrQlre3otSetIGdHNS4fIY6Kr6qqytQYuKFDh8Lj8aCrq0uxvKurC7W1tZqPqa2tNVx/6NCh8Hq9OOmkkxTrnHjiiXjppZesvIySxgDXj2UT3swe4rES3Iw6mlhcf//0eD3G25d3QFqdj5mOx2yIy7qDUYU3o6qbleCmVW3TCm3ywKa4XxXSsj2Uqg5t4rbFYCdEehX3yffPKMwJHp/0WuVBTnzd8iBXqBCXibpd5PJFKFNos9qe1G1J/Rxm2hJgvhpXqBDHKpxz+P1+TJs2De3t7dL4tEQigfb2dqxcuVLzMbNmzUJ7eztWrVolLXv22Wcxa9YsaZszZszArl27FI979913MWbMmLy8jmJggHOAbKpvdoU3M52NlY4mm8CWaRt6gc6o89HreOyoxBWk87A5vGUMblYPp6YCk3w7Ln9Qeh6Xz6cIefKqnBjiFCEvdXhV66QGyMbIqatxeiHODmaqb9mEN7PtqVTaEmCuPYmPLdTJQmSe16Md5rPdllVNTU1YsmQJpk+fjpkzZ6K1tRU9PT1YtmwZAOCiiy7CqFGj0NLSAgC4/PLLcfrpp+PnP/85zjrrLDzyyCN49dVXsWnTJmmbV111FRYvXozTTjsNZ5xxBtra2vDHP/4Rzz//vB0vsyQwwJU4p4U3qx2NHWPg5NuX//HQ63yMqnFmQpxaoQ7z5HJ2qHp8G6BfXcs5vImPsRiY1IdWxX3RqtQByeAqP9khn8y0Q6vhLZvglim0WW1PubYlwLi6bVeIYxWuf1u8eDEOHDiAtWvXorOzE3V1dWhra5NOVNizZw/c7r5fpNmzZ+Phhx/GT37yE/z4xz/GxIkT8cQTT0hzwAHAOeecg40bN6KlpQU//OEP8cUvfhGPPfYY5syZU/DXly+cB67E2R3g7Bqfk21ws2vcjpzWIGytb4Hq9eSdiLpzUHc66g5H3dFodS7qzkPzygkyeuPfMh1CNTvuTW/Mm94hVN3Dp3qBTiewqQOYfHyc4jCpeMKB7CQH6f5UJU1xYoM8vKUqcIr75YdJ1cFQPWecqqqnPolB3Q7V7cHu8GYmuBWqLQHp7cmoLQGZ25PWY/q2rb9/2XxRckqIK+Y8cK/u3mvrPHDTJ4wu6OsoV6zAlTCnhjd1Z2PU0YRj2V3cUX7dPq0xO+I+qKsIetU49SGgTJU4dbVAe1ydsgKQ8PiUIc7jU4Q4we3tC2tevxSUxLDhSsT6Dg/Go32hJfX4ZEUqdb9sP6SzPP1BzVDm8vqlQCdfR3EigvwQq05FLG27PmUISgty8nFpWqENMB3c0tYxG940xr4VM7zlEtzy1Zbk+yG2J3VFTmuIQraVOCIyhwGuH8n2cj6K9WzsbLQ6mmw7GaPtaHVAekFOq+PROwRUsBAHSEFOHtak4GE2yHn8UlVO8Hj7KnMen1SZcwUHAYmY8uSCSK8iQAkalTSrU4qoD4VKy3WmEAEghTXpOXMJbUDOVTcgv+HN6ItQsdoS0NeetA6bxuL2fSlSr9+3TXsPpVJmbhvngXPbtB3KjAGujBWqs9HraHpz6ICCstCmFebMBLlihjgApoIcALhUrVR+JQJF1S61nb4zMlMBJh5JGyummNjXV6E4NOsKDuq7L9fro+pc3koMXoJimTpgqQKY+ooMGQKb5jomKm6AdvU7X+HN6IuQ2eCWS1sCtNtTNm0JsNaetNZPbtO+EMexcNRfMcCVKKuHT3M9dGpHeMsU3DJ1MtGE/v0+t/KvvnpbYgdkpvPRC3HJ50k/BJRNiAPUZ+Sl9kMjyIncOtNZpH18flmVDoAAVdBRV8t8FYr1tbaZ1QkS8ktzGVwpQY/WtU8Bg2ua2ngNVL2Jes0ENyA/4c3oi5DV0GbUlgDj9pRtW5LfZzXEaeGZqUTGGOD6uWKENzPBLVMHY2Z9eSckPodR55MpxCWfR8g6xAHmg5zIKND1reTTnAhYLwCJgU19kkSuQ941r8mqwTBQZSOLOdrMXknB6IuS2QvU2x3ejL4I2dGW9B4jticzbQlIhjUrlW25bM72lmMVjogBriTZWX2zys7wpu5s9DoaK2N55OPd5Nsz6nwydTxG1QN1iAP6Oh6taRSMgpxc3zbNfXZx9XFUM7zpj8npd8VE1SRXto1vKtL0O/LwZnb8aLHaEqDdnqwEOTOVbbMnNuTjCihE/RkDXD+Wa4ej3FbyPjs6m1wGX+sNuDbqfIrV8Yi0OiCrQcrMSShWLpRudj9yvWi6maqKMrTJzoI0CIyZtmtHp2/0nmc6eztf4c3OtqR+vLotAcn2lOlLUb7bkh6e0GAfn9tl2xnBPLO4cBjgHM5KEDAzK7zWutmGt0ydTS7hQPwjoe6AtIJcoToe9b5J62bxMrO9pFkpnWwiHxgvF1AtV5/9ZjTnmPS5I/3wNCCrjqqe065OJddpd7Q+D7uCW7btSa8tic+r96XIrraUiV1VOB5Gpf6GAa7EZHvR+rTtWNxMpnE6VsKbUWdj1MkYBQV1GNAKTPJKmzzI5avjMbrmo5rP7TLdwZoNBJkqOZk+G73PRetziBmlfR1e1Ruj/gzloUr8TOTjGsX15YFP/FykkBfXDniA3kkkmfc7H58VYE94M/slKFPoDioOnZpvS+K21ZXtXNoSq3BE2WGAczC7qm9mpzdQLxP/gJvpbNTPb7Wqo7V+UKoSKMeiyUOauoJgR8cDmLt4t1yulyoz6vzVn4P4GYSlnwXF/WIY640r1wOAiGynjkaUJbuIhc/MrwprFf6+sprfowxmwdTPYuALSmHOpVhP/ByDXjcQ7Vsur87EPX3jGbXGKCqnOE4yE8DVrF5mLpvwZiW45dqejNoS0BfkjCrbem3JDhwLR5SOAa4fylR9M1NMMdvhaIU3q8HNSnVHXtVRj82Rdz52dDxm54oTZVGkMtXpmwlsWmFNHtTE+8WAdjQSlwKZGNSOpP41DnHGF+P0q3ptreA2ILVMvE8MexV+j2a4kwc7n9uFzyPJ+w9HkqFOXqmLx1InqUDo+xzjQtphWpHX7crqc5PL5jC2HV+E7KiUiu+tehoRvS9F6sq2mbZUSlU4HkbV5nW5bKtkejmRb8EwwJUQuw6fqhlVFIzOOgW0L+WTTXhTdzbZHJJTP07d+ciDnB0dD2A9xJl+HVlW2rSqbOrQJq+sReIJKYRFYglFWJOHueT9cSnEiesDQDiS/ksQiyqXeX3p5ZaAPLx5leFNDHry0DbA71GEugq/B2FZoIvF+8Kc+FmEkUA04U77XOVhDgCQqsxB9mvnc7sUn0M2n2U+w1u+25L6sVrtSf2lSF3ZztSWRGamF8k0PxyrcERKDHAOZXaOqrT7LRw6NRpXZbbDka+n1dH0ZtH5iIfc1J1Pth2P/DVmE+KAzJ2/1udl9hCbOrjJq21alTat0KYObPKwFoklpJAmBrN4TFD+bPFz8njc6En9Xwx3Hq8Ln6V+DsiCmjzUiYFugN+D7iPR5M+yMCd+jmKYC3rdiEbi8LlTQT3SV20FZOPn4sogp3WI1UqYy/R5AsYnkAC5hzd1e8qmLQHp7Uke5NRfiqy2JaM5FwHjL0G8Vir1F//+979x6NChjLcHH3zQ0nYZ4Po5q2emGY170z7zTXu8FaDf2eh1NEZTIsgHsssfL+98su149E5s0KPVseh1/npB287wplVxkwc3ALLlqTAnq67FonHdwBaTgp3FQ3O+vs9L3JbH44bX50E8FkMsGofX50E4tS9ihS4Si8Pv9SAST0iVOYkfiuVBAL3SE7oBebU1pjxpRR0oMlWEcvk8AWsnmPRtI7svQlrtKdP0IlrtSSvIqb8UWWlLffubPh7O7iocD6NSKRs+fDiEVNt3uVzw+Xyorq5Ou1nFAFdGzFTfpPs1DveIMg2Wl69jFNyszGGlN/+bvPNRdzxGIU6+n2Y6nkzXe1Q8LkNoLmR4kwc3AFJ40wpu8tAmBraEPDhENcpIOhKx5Bvn9roRiybg9bkRQ/I5PB43YtFklc/jTR3qVgU5AIAfgOxqXdL9sitqBSELGjmGOEB7uhHrc/Zlf3awlfAmb09W54PTmjJEK8ipvxRZCXGZDqVKr4lVuKLzuM2doW12W6R03HHH4fPPP8eqVatw2WWX4ZhjjrFlu3yrS4SV8W/ZHj7NuA8aYU1k9kxH+TpGnY26w4nEExlv6v1RdITx9E6uVxGAlJ2n1kz24n1mL3GU6yS3cupDbHodstZzqg+b6pGHN8Vzq8KbyCi8xeOJtJt6m/JtiIEwFokr1o3HBN1geDQVQI9E4oavLZcxYHYwddaqhS9C8m3K25L8ULk8uGfTluTtSastidtXtGGdtmTmdWsdQraz/RCVsg8++AC33347fve732HSpElYt24dDhw4kPN2GeD6MTN/IM1U3zKFNzmtqSr0Ohu9cKYnU+ej1/GoX6s6xGmdzWcUZvNBfYhJXQ0UaVUjAl43/B634oxPLQNS48kCqvW8Pg884lQesvvcsn1Qn6Tg8bjTbvJ1xfXFbYiHVL1+j2Jdj9eleQIEAMV4OKPXpp5vrpDsGPemt00zX4REVtuS1mP0vhSZCXHqtmQU7rTalvxLaaaXkOsXVaJC83g8uOCCC/Daa6/hoYcewssvv4yxY8fikksuwa5du7LeLgNcmdA7fCrdn0NgyTTPGKDd2cgdTVVZMt30tmHU8Rh3lpk7HqMqXL7pXc1Auj9DeBGDnXp6DyAV3Lx9gVAe4sTA5fa6ZSHMowhn6m2p79MKb3rPHUiFywEGQU08mSFtbBy0JglOXyfT2MZ8MfqdMTtnHwDNL0J2tCVxOyL1l6K0/bAY4nKtwpmt1Nl5TWiifJk3bx7a2tqwdetWhMNhTJ48Gf/xH/+Bv/zlL5a3xTFw/YRd30rtniRWvq5WR2OV+BixIiNu0+9RjmPrjSekcXHyMXFG4+HUzJzQkA35TP/i9uNCct6yWOrKAnFBQCA1nijodQMx9LVWcbyXN/W6ZNv2V7iTA/1TYedoJA5ExOUe+L2pExmkkwaSJxIEKryKMXEejxuBCuXZp/LDq/4K/T8d8qAmhULZmajiz+ozUdVTi4iVN/U8cQGvWzH5rzg/HJCsRson/O2b7Fd1BQcoA53d46zMVN+yDW8io/n6zDBqS+JzBbzutLZkB6tj4ezGExmUeC3U/Nq/fz+6u7sRCoWkf0OhEKZNm4YBAwZg8+bNePrpp5HQGNpjhAHOYcx+y7Tzm22mx1sNb1qdjZlZ/uUD3LU6n2xDnJkz6bLlVc01JhL/yMmDnBjiAEjTXsj3IwhxsD4QQOq9TQU5byyBoCd5CFk8pCoGueoBPunEhmTn7FNNNSKerepLm/stbexbzPh3Rl5VA/qCmzysAUibOgSAFNoAKKYPkU/uaza4yd87dXgzG9zEMGG2zeWrOpuvK2aIn4VeWxKfUyvEWW1LWtOKGMl0NiqRk4wYMQJutxvHHHMMjjnmmLSzTy+++GKehepU+ZrAt2/7ff83Onyay9g3LZnCm5VLM6nXl3c+RiGuGNRVBK2qgvg5KAKE/O0QJ55NBbkBPo+iKpd8bEI2QbELgEc6Q3WQPxmoewN9n1n6xL3KiX0Bo8/KeoVHfthWHtIA81djUAe2vtdqb2gzqvxYDXJy+bzcWS6XOpOvrxXksglx2crm8ZzUl5xk/Pjx2L9/P84//3ysWrUK48ePt2W7DHCUFbMdjpXwZnQYSD2IXT7lhFGIs+NQavL1mascmD0ElGk9H5KHWT2pFhoXBHhT1/H0uFJVLdkM+JV+ew7NmTnMbRQU1NdABbQvpyXf/6yuhYr0sJbcBtKWqcNBtofptIKclQvfm2U1vFltS4DyM9EKcuoQZ4ZeFU6PlSEKZkMeL25Ppejdd9/FE088gVtvvRVf+MIX8PWvfx1NTU346le/mtN2GeD6gWzGv1ntdMxU37IJb2bH7sjXk8KaTogTqUOcEbOdjhGtjsPKYSD5mJx4In17UnDwiJ+fGHCS/1bBo3vChdbcY4DxBdOT9+ceTtQdrzwwy082kL/v8nW0App6uZmQlu0hOfVZkXqHxcXD4FZpzfemxUx4s9qe1EFOL8Rl+kJkxMphVKProxI5lcvlwjnnnINzzjkHW7duxfr16/G1r30NU6dOxapVq/Ctb30LHo+JCoEKA1w/pNfp5nL4VItWRUdLruFNa3tGIU6vcpBLp6NFHhrUgUH99FYPEfk0d085kbAerY/D7BUE0rZlw7guoyqL3vuiV0XR69DtHjgtf1/E57Rrurlcvgzloy0B2l+KpOfRCXHK12Stok2lxe2y75A0C6DG6uvr8dvf/hYffvghWltbcfHFF2P16tVYuXIlrrrqKkvbYoBzkKzG4NjU6QDak/aKcu1wjpjogORTTJgNcUYdil2zvMvDRqbgpvdH0uphn4DHxbPoZLIZR2o0Ibb8c5NONHH3tSd5Fc6Ow6hmvwyJMoW3TO1JPV2LVnvSqmrLGX0hslrR5hUXqNyMGzcO/+///T9cd911uPvuu9Ha2soAR/bTumqBKNcOx0xwU68rdj56IU6L0VQIZqoGWmOr1OSblXdG8tCWXqlT/uyOR9M3rLHMlYgpF8QimdcB4NLaPgDE0x+vfFz6towIHhN/Wjz+tEWCx5e+zK2xLa/ysR6t9TS2lZAt87hdacEvlhCkz0scmqCY9kUnxJmhVc02Cn5GX4ZE2XwRkq+n96VIvSzXL0R6Fe18TdVjBqcSoUJ688038ac//QmvvfYaDh48iIqKCowZMwZz5szBD3/4Q1x55ZWWt8kAV4bMVgvMHD6Vs9rhWAlvckcicc0QJ3+OTIOwcxnrBvQFNK3KmXifXnCTdxqKwJb6vyJ4yYKZfLkUxGTBSwxZipCWeowQkS77DiEs+79s+/J1AECIpoc99TpqLn9Qe7nPp7ueKxXGXIGgdJBYsZ1UMJOHOykgykKgeL8iyMmCnuD29r3fqXXdUIY6kTrIyad9kYe4fNC6OL2c3kkk2bQneVtSP4eZL0RazHwhMntSkJw6IPJMVHKCp59+Gtdffz127NiBL33pSzjppJMwceJE9Pb24r333sMjjzyC733ve/jOd76Da665Bscee6zpbTPAURqjsTlGh0/1iB2OmfCmdykgdRDT6nj0Oh2z43LEwz7ZEHfPKLyJwc0wtKUClfizOqilhTRZQBODWSK1DTFsCdFoX/BS3yc9Jrm9eG9foEtE+gJjrDec6S1Q8AYDip/d/uSfGk+wL1C5vcng5Aokw5rLH5QClxjgxODn8gfTgp4U8txeCB4fXEgGOxcgBTtBfH/cXrgQ6Qt0iZgU9MRPPOHxpVVkxCBn9+WbtK/4od+OsvkylE1bMvpCpMXOyX2lbfJM0oLzul22vef87Prcfvvt2LRpEy6//HK0tbXpzvX2yiuv4Fe/+hVOOukkdHV1md4+A1wZ0zuBQYvVDseqTNdwNKqmZRqrIzJzNmou1OFNN7hZCG2KwCYLa4lU8BJikWR4S1XLhEgvkFoGJANaIhaVglkiEkOsN4yY7Gfxvng0uf14KqzFZQEu3qusxkV70w+p+oLKPyeeYDJ8efxe2bIAPD5lmHP7vfAG/VLoc/u98AT9ioCnDncun68v5Hn9Ugh0+4Opit3RZEUuValzyQKdGNxcqd0Sf+vVQS6eChLyECceThWrcOL9+ZhOBEhvF1rVNyvhTbwvmy9ERodRjcbBEZWrRYsW4Yc//GHG9WbMmIEZM2bgmmuusbR9Rwa4DRs2YP369ejs7MTkyZNxxx13YObMmcXeLUL64VMzHY6Vi9nLOx6jTsdsqMtl8HRfQMu8rlF40wtu8iqbvMImD23yypq4jhjY5GFNrKbFeyOIR2OI94YRj8SkYBbtjSGR+lyivTHExSsxHJVX4cyPg/Omwpw3dcktT+qz8AW9cMv+7wn6pIAnhjtP0K8IdW5/T1/Y8/oUgU4Kc2IIDvdK97sSMbjiyeqcGK3kn7SQejkub+qwazwKeHxwx6OGIc5INlOJGA1H0GN01qmZ9mTHF6JC4FQi5GSjRo0CAHz22WcYPHhwxvWPP/54S9vPKsDt27cPQN/OFdKWLVvQ1NSEjRs3or6+Hq2trWhsbMSuXbswfPjwgu8PmZPtNAe5sjoZqZrWAGujwKdXfQOgDG+yqptRxU0MZYLs0KcU3EyENjGwRUI9iPdGpaAmVtDikThiR2OI9cYQjySkkBaPxBFJXUbrqCy1RExUmfyp11yRejP84nVQ/R54g154/KmJe4NeeCu88Pg9UrCTh7pMgc4ozBkFOVc80neI1e1Nhmh5NU4jxMmpq3ClxOyXIXHdXNqG0XQiivVyHG9K5HSzZ8/GM888g7Fjx9q6XUsB7m9/+xsuvPBC7NmzBwAwdOhQLF26FNdccw2qqqps3TE9t912G5YvX45ly5YBADZu3IinnnoK9957L1avXl2QfSgldo/NUdM6AzWbioEeKx2OuL5Wp2OlapCPcTtaoU5rzJviRAT5yQoZwpvWYdLE4UNZBTet0CYGNjGsRRKC4v8AcFT2WamDnF/1+itS76/f7UJFNDXTf28MFT0R+H0eePweRWAMDg7C4/cgEYnD3RtLBrlILBnkojHpsCsg+6MV7Dvs6ULfoVA16VBr6nMQT4CQfk6FOGl8XKoSJ2f1jNNsZRpPqpbtiUB629I6oYH6P4/bvmmJeGZvuq997WuYPXs2nnzySUydOlVx3yOPPILzzjsvq+1a6sUuueQSnHjiiXjllVewa9curF+/Hv/7v/+LqVOnSlW5fIpEIti+fTsaGhqkZW63Gw0NDejo6NB8TDgcRigUUtz6MyuT+FIByU9ckJ9ZGo+qziTVD29CpBfCkRCEIyHd8BYNHUHk8BFEDvdI4S0cCiMcCqM3FEa4O4zez3rRs/8Ijhw8isP/PoJPQ2EcOBJFZ28c3dEEDoTj6OyN4UA4eetS3fYejZpa1pV6fGdvLLW9ODp74zhwJIpPQ2Ec/ndyH8KhCHq6jqCnqwefd/X07e+ho4iEjiZfRzSWfG2hI+g9dDj12iJIxKLJ90lekRTfq1ik7z7ZYWpXPKZ8z3VCtdaULlpnPXLQtj3yMYaQnEkQBKxduxYjRoxARUUFGhoa8N5772V83IYNGzB27FgEg0HU19dj27Ztivt7e3uxYsUKHHvssRg0aBAWLVpk6aSBbG3YsAFXXHEFzjjjDLS1tQEAnnrqKUyZMgXf+c53st6upQD3/vvvo7W1FVOnTsWECRNw0UUX4dVXX8WUKVOwatWqrHfCrIMHDyIej6OmpkaxvKamBp2dnZqPaWlpQXV1tXQbPXp03veTylsxOnT5ODeRWHlTrpesuulJVt4SUoWtV1V9MzqEGkkIadW6Xtky0dG4oKjwqcVlh3j7Xkv6WbDyEzCIqP+45ZZbcPvtt2Pjxo3YunUrBg4ciMbGRvT26k9jJA6vam5uxo4dOzB58mQ0NjZi//790jpXXHEF/vjHP+LRRx/FCy+8gI8//hjf/OY3C/GScNVVV2HTpk1YtGgRpk2bhnPPPRennXYaPvzww6y3aekQ6oknnoj9+/fjC1/4grTM5XLhpz/9acmeRLBmzRo0NTVJP4dCobIMcYU6BEQmpkHw+pUT73r8aRPp6s2n1ncF1NS3r9Rq3qAf6lMMkgcEjyIKQDwgGBwcROxoDB6/G/FIAp7eGPypw6d+dzJY+d0uKXT5U2Eu6PagNyGgwsQRtmDqtfeNhXMrxsX53a7kv6lDqeK4OHFMXLAqoD0eLm0snFcxLYnR+ya9fx5f2iTD8nnjNCcNltHKnGxX9uCVGPoX9dGuQCCAQCCgs3YfQRDQ2tqKn/zkJ/jGN74BAHjggQdQU1ODJ554QvdwY6bhVd3d3bjnnnvw8MMP42tf+xoA4L777sOJJ56Il19+GV/5yldyebkZbdu2DZs3b0Y0GsXbb7+Na6+9Fj/+8Y9z2qalALd06VJcdtll+MMf/qAIQd3d3QUZAzd06FB4PJ60kmdXVxdqa2s1H2P2l8ap1GfG2T2dgc/tNrwSgyjgdWc9Ds6KXAZdF5pipnePD4hHk/ORyeYmAzTGZgFAImYpxPmRnHst1htGzJ8cPxZBcrknmDzb1J0aYxb1e+Ct8GoGOQCqsXDu3E9iSAU2ALqhzfqJDF74Bg3UPolBNb2IKzW1iOIqDx6/8mf5lR1Sy7Um+M23oMdteRycXTj+rXy541Htq8BkuS0AaYWS5uZmrFu3LuPjP/zwQ3R2diqGSlVXV6O+vh4dHR2aAU4cXrVmzZq+/VANr9q+fTui0ahiu5MmTcLxxx+Pjo6OvAa4r3/963j66aexaNEivPnmm9i/fz8WLlyIcDiM6667LuvtWgpw4mHSiRMn4pvf/Cbq6uoQj8fx61//GrfcckvWO2GW3+/HtGnT0N7ejoULFwIAEokE2tvbsXLlyrw/PyUFvW7DqzRoqfB7NM9E9Xvclk9k0Nu+Fq3AZ+UEBq1L/WidaScu05odPpE6q1EMccmdSFbhpBCXWlc93YU6xLn8QQg+X/KEBjG0RHrh9ibPRE2GnGSQc/u9ihMa/LKpQ8RDlOoTG4DkYdbK1GFW+VmpcuIZqnLi2aZA37Qh4nQiWmeeArAc2tLOPk29l/LwpghugBTe5FdvUFy1wSs7I1X2mQF911jVqrKVwhmoA/weW09kILLL3r17FYUds4UUcTiUlaFSRsOr3nnnHWm7fr8fxxxzjOnt2iUej2P79u2oq6sDAHzhC1/ASy+9hMbGRuzZswf33XdfVtu1FOA++eQT7Ny5E//4xz+wc+dObN68Ge+99x5cLhduueUWPPPMMzjllFNwyimnYMGCBVntUCZNTU1YsmQJpk+fjpkzZ6K1tRU9PT1S2ZSKSx3I/F532lxw6k7HbIhThzGtioE48Wix57ESD6OKVTh5iJNCmheKEAf0nU0peLzJwfYeH1xubzK4iZPzev3JAfqpqxSIg/bdsQg8qZMfAoDmSQ4AFKFOPmmvfE645Hpxxc8idZiT88jed/nEvmJIA5QT/Kon9jWa1NdoQl/FVRoMghuQOowqvufq8CaruqnDm1iILOXB9la+EOVazdaaFFvrMCinEClPVVVVpo7MPfTQQ7jkkkukn5966ql87lZRPP3002nLJk2ahK1bt+aUlSwFuJqaGjQ2NqKxsVFa1tvbizfeeEMKdn/4wx9w44034tChQ1nvlJHFixfjwIEDWLt2LTo7O1FXV4e2tra05E3meT19V2PQo3WI1Ju6OLwWvYqbnkwdT74OneYy7qYvpCUnG1VX4YxCnHQ4VZx/TJ6RPD644lEIqbFxYphzidW6VEDRmtwXkAU6AF6NqzIAkIId0HeZLHnAE9cRiVdpMEs+7Yfi8lmpSXu9qWViZU1cL+3qC4BUYQOgewUGl3QFBtk1UT1eRWgDkDG4qStvVlmdxBfQrmhnGpIgb1/ZfCEy+jKU6cuPmcvS2cVBIyYoS2effTbq6+uln8Ph5N+jrq4ujBgxQlre1dUlVbDUzAyvqq2tRSQSwaFDhxRVOKMhWPlWW1uLv/71r1k/PucrMQSDQekyEIWycuVKHjK1gXrWePFnq+PZzI7bMep0gL5ORVHB0/kLbqXDke9noWiFOACKi6gDqWARjwL+5Ng4IXXqgThxr0usCMWjffOciddF9VXANVD7uqgAFOFOsTwahU/2M3QuaC+/6L1IDIBGxBAmEkMWoDokrLruafI1+RTL5JU1xbqqsJbQqLIB2he2VyzTGO+mddhUXX0Tf0XFdaxW5cy2MTGMiW1Hq6Kt9ziR2J6sfglSV7ONHq9VZeNltMiMyspKVFZWSj8LgoDa2lq0t7dLgS0UCmHr1q249NJLNbdhZnjVtGnT4PP50N7ejkWLFgEAdu3ahT179mDWrFl5eW1f+cpXsGDBAowdOxZLly7VXEf+2q1y5KW0KDdmT3TQqgwYPVbslMx2OnrjdzJ1NHqDrfU6HK2KgV0djl4VDlCGOACaQQ5QhTlACnQA+kIZ0Be0fBUAVPOXyQcgB+XzysUUl49SrJdQVtXk4U1aphHirJCHN8VyeZBTnfkpP7lAPPyZvEKCXwqxguoEA62glrYcULzvWqEN0A5uQP4PnRpVtLVk+kIkstKerA490BpPmmuFTu8Mbp6lmkfxqHKuyly3lQOXy4VVq1bhhhtuwMSJEzFu3Dhce+21GDlypBTOAGDevHk455xzpICWaXhVdXU1vvvd76KpqQlDhgxBVVUVLrvsMsyaNStvJzBs2LABV111FX7605/qBrhcMMBRRlpnomZzIgOQfmjV6iBsdXizo8OxQjypQetEBnmIAyAdTgWUQS5tm6njqIozVmWks8NSwU0kAKb+WLoSJg5/xgo8n5rXn3kdZJ7WQ/1eyRmdRRpPCIBOaAPSpwuRhzd19S0XWl+IxIq2ukqn9YXIbIjTYtSWMn0ZMjv+TU08IcjrUf6cDa1JlS09noGwZF199dXo6enBxRdfjEOHDmHOnDloa2tDMNj3xe/999/HwYMHpZ/NDK/6xS9+AbfbjUWLFiEcDqOxsRH/8z//k7fXMW3aNDz33HN5275LELIYtOFgoVAI1dXV6OzqKtjlvzKxMt5G82w4g84muf30x0qHglIfvzgGTvxZ7DjEkCYGuHAsIT1WvE+sGoiHUcXHGl3YXmt8nFHno1V1y6bDEQOcWIHzuV3SOmIFTrxPXK7X6cg7LHnVQN23aV5mq0B9RzaXWstXpSnbCkoh3iu990mvLQHah0/NtCd5W5I/vjeWMN2WgOzbk9m2JF9u1J7kbUm+js/tNt2W5I/Xa0vq3x+93wuzE2mXWoALhUKorakp2LRc4nNWV1dj/94PbXvOUCiE4aPHFfR1lCtW4MqI0WS+4okM6nFxWrKda06vciCyMg+VXodjhZWKgRViJU6kfq/klTktRu+t1hE29WeaFjpUn6fWCStan7md8/ppHVZTv7dej/H96s9Lq6M2KrCa+bz13nv1+55L9c1obkXxMKq6CqcelqCWS3vSq2Kb/TKkxepwBB4eJbKOAa4fyGUyX73AJnYc4qFSo8OoVjoddYgDtKsHetSdjdVqgbjfetQVAyPyw6jqqy/IO3x1P2dU1ZHTqpgCyrAlD2N61dPk47WrPur15GOwjE5MUYc7o/dLfbhN3vGrqzfybakrovL71JUc+TL19gDV+EMZo8Bn5nNRrG/xYIbYTqx+IdIbW2q1PZlpS5noVd/swDNQC8eViJsbamFyW1QYDHBlKtsqmtVOJ1OIA5SdhbrzMepIjMJbJlqHe3KldwmtTOPSjSpoWmHNrsPc8rN91Ye51Z9DNpPF6g2Ot3K4O+Phuag81PU9d9wjpJYlF2qNWRRPPrEiU7UzF2a/EAHKEAcg5/akF96yqb7JX4/8sWbGv+V6HeFiXIeYSMsxxxyDk08+GXV1dZg8eTLq6urw5S9/WTGOL1cMcCXAo1MdKDT1YVR1FU5Lpk4H0A9xANLOTjXzzV99yFQrvGVTfTOiPrynR37GqdH9ctkENq2KmnoMlXz8lDygif8/EolrjFPsu09cXy6cIcgFDKo6YqDzp97MCr9H+swG+D19n6PXrfhM5Z9lX6iLS+MXD0eU1bqA1414LPl7HEudt+r1APF4+lU1tGh9drmesKBX0TY7nYhRiAOyb096bQmwNo5Uvl4+Dp/yECs5zSOPPILXX38dr7/+Ou68807885//hCAImDhxIurq6hTBLtt5bBngyoDH3VcF0hoHZ2bcG4C0TkeswlkNcQDSOh4gvfOR0xrnptXZANl3OHbOGG+mw881uKkHv6ura0cjcUVFTR3YIrG4IqiJ4SyWukxWPCbbJ41LZ2npSf3rlV9Wy5t8vw+nlgVkQU0e6rQCnTzMhVNhLhZPBrle9FXWwkj+viGWfD/EIAekglw8FeKEvsqc/GxhOTsvTm++bRm3JTm9EAeYa096Y0aNwptVetU3uxTqJCCibC1YsEC6ysJDDz2EG2+8ERdeeCE8Hg927tyJtWvXIhJJTZ5u4nrjWhjgHMboRAQ5M4c4zaxjpgqn2D8TIQ7QHq9j9mSEtLE7BuEtG/nqdER6JxmYOUSqF9zU1Tax0qYX2tSBTQxrsWgc8dT6MdlnE4sqP/uE6nfBrfHZeX2pAJ36vDypzyTs88DjdaEHyVAX8Hvg98Zx6EgyuPm9HkTiCRyJxDHA70EkloDf68ZRxFHh90hBLpgKcb1IBYZYou8vmmw4jxRC4n2H8MwEOavkYc3o6iZ6VTgzX4gAaFbigPTDpdm0J622BNhXfTN7+JTj3wosHrFvKqF4gackcoCrr74ajz32mGK+ub179+Lss8/O6TKgbCb9RM5zIpmcn0n8Ax1Q/QHXql4FNb7F+z1uRSdRkaq0ZDrUo7Weelta4a0Y1TcrMlVn1OFNvlw96avV8BaLxqXwFovGEe6NIh5PIBaJJ2/RBGLRBCJHY0jEEkjEEogcjSJyNCo9VryJy8X1ErGE9PjenihikWQwjMcT0nNKzyurFB5J7efR1L4ficQVlUTxdYZjyeAqfw+iCUF2SDkhvXfiMnVQVsv3RL3ZVLOk31+N33OjtmTUnvTWsyu8Wam+FevwaalNIUL9mzhdi9zo0aNxxx13YPPmzVlvlwGuTMi/0cq/6Zr54ygFI9Ufar0QpzjLUBbijIIckN6x6HVGWsEtm/Bm9FrlnY7emY75pDcuSh0yxOqb0bUv1eFNTgxRophUlUuuJ1baxKCWiXw9MciJ2xO3LYY4+WPCGvsmkoc4vTMss5lU2g7ZTkVjti0BxiEO0G5LgH57khMfq/dFyK4xpMltJP/NtQ3lOv8bUaEtWLAAa9euhXra3eHDh+Of//xn1ttlgCsR+fhGmO03Vyt/aI1CnF7Ho9X56HVCRuuot2UmvMnpVQzkjMKbnZUBvaqQFWanj9A6ESGuEQDVh0nV66tvcnphL6Z6bvk4O7WjsgqiESuXn8q3TMFf63fMaojL9KXIqD0Z3W/2i5B8H+2qvvHwKfVnra2teO211zB9+nQ88MAD2L17N3bs2IFVq1bhhBNOyHq7HAPnQHrj4NTzwaXd77Z+MoPeGala88Kpx/AAyjFxQN80FvLxPHJmpgFRd4JanQ1gfY4x+WsuFPH9VY+Z0hp7GEiN8xKrcF6PG4oT0v19IU6qtESSy/XFAXjg9XmSoUu+rs8tVeLUfyjkJylokY+Jk4+FE8fBeVPj4MT/B1InLIhj4MTX4Pe4005okJ+ZKr0PXrfic5aPxTKaO04u20CuHkuaaSyc0bhSrfFwABRj4gAoTm6QvxdW25NRW0ruv/XwpiXb6hvPPqX+YNSoUdixYwd+9rOf4aqrrpIuAXb88cfj17/+ddbb5aW0Skiul9QCzF9WS70NM5cDAoznHlNvS2+CWMB4kliz8jlBbHJ7fds2W32zMgVFLpdgEqcNST4mkXYoVWssnHyqEPnhVHE8HKA8pCqvqqkrZ+qTGsSQplimOnlBDH0er0v6vxjcgL4TGOTBDYBueJNPKyIPFeopRQDrE/8C1qaCAaxNtgzotyX5fWYmXAZyb09m2hJgfNJCpnnf5MuyvQydHYdPS3X8WzEvpXVw9+uoqqy0Z5uHD2PohFN4KS0D+/btA5AMdrlgBa6fS6sO6FThtM5INbq8ltaZdAAMqwcAFBUEkZXOR+vs0kydDWB8llyuh04zdR7y++Wdv/w9l1fiAEhnTCr2TTyz0gsgloDP70kGOQCDPG4EPbIzUv3K4HM0EscxFb5UmEue9QmkwpzXDcCnCHSAcjqRQLDvwvCxaByBCo3XqarKiRU2+X1WpxABYBjcxPcx1+tvKl6Hyc9THeS0Pk/xecUQZ6YtifuunjBbXdkGcmtPdrYlM4dOC1nZJipluQY3EQNcCbEyoW+2h1FN7YeJQ6lA5o4HgG6QA7Q7H7PUM8GrD99onbBg1OGIrzG5baQtU29X2g+L3+bVYU6xvVS/qxfkpENvcMPnTlbkApAFB68b3lgCQU/fZL5iKBIrcNUDfGmT+crvF6kn9O1bnjkcaE3em1zeV1EDkBbYxMcaTeILwLDiJi4DrAW3bAbAawU5KyEu17aU3K4yyAH2tyer4U253fTnyrb6RuQ09957LyKRCJYtW4ZAIGC47ltvvYUbbrgBv/nNb0xvnwGuDGRThdMam2W141FvU6/zERkNRte7dI9ecBP3o295buHNSodv1H/KX6JemPNB9nmlLgnlRXIy2gE+T9pn4HP3HYJLbiNZmRsE5XQjvQHl4e70qzAofwa0L6Gld2KB1ngrK5fTMpqA2eqg+UyhTe/zy5R9tF66+suUHSEOQNqVGqy0JUC/PRldBitTcEsuz/2LUDZ49ik5zVe/+lVcdtllWLNmDRYuXIivfvWrOPnkkzFkyBAcPXoUH3/8MbZt24Y//vGPeP/993HNNddY2j4DnIPZVYUzeyg1uW3zHQ+AtAqCuL5IPq7HzPUV1Y8XqQOWVoejNeZNfE3i61Qv09q2Vodhtuihv57OvHvoe+883lR1LrXM40odltQYxK43RlFrTJXeBey1pjIxc1aoVpCzerZwrmOs5NsCcvvM9B6jDuN2hrjkNrSHJwCZ21LyubJrT2bakvxx2balYlTfSnX8W7G54jG44lHbtkVJEydORFtbG1555RVs2rQJ1113Hf71r39BEAS4XC74/X7MnDkTy5Ytw4UXXohKi+MQGeDKhFEVTk+uHQ+QXo0TaXVA2b4uuUydjXqdXMObXh9ppQOKJoS07YgdjbxSCqhOPHGJ/0+tKwgIeD1SpU5cJlJPBNz3/MpfBLNz0BnRev3qQ2xWPh+g+J+RmroNqT8jqyEOQNrZqeq2JK5Tym1JzmoFVGs/ktvRXZ2o5M2YMQMzZswAAHz66ac4cOAABgwYgNraWvh8vgyP1scAV2LsurC9VhXO6NJZmQ6lAuY6HgC61ThxPfE5RLkGA0C/swGyP9s0m2CQTeXA6DHykzuTQU92WAvKkCeKedLfz2hCwADZxrSuAJFpLjoz1/QEMh8u0xoXpfWYXCqfdldw9H5fxefXq8ZZPbFBPsUIkN6WxHUA7bak3j+zr0nOrqCd6WQR6bE55E4ePiWnGTJkCIYMGWLLthjgHM7stVG1aFUQcu14gPRqHKDf+YjPlQ2jjkbcD611cw1vZoOb3VUDj94GZa8hGdqNnzibS0ZZnaUim07Z7O9BoasxWl+EgPQgV6y2BOTenowmGBb3Q2/9TOFNzeqhU7s+bx4+pf6GAa4EFbIKl8+OB4CpzkdO6/BdpmtIGgU39eOthrdMVTcrHU5/mKvKqXKZY1H+ecrbU77bEpBe2U5uq++X0Kg96R0KN2pPWtPs5PJFKLlNc4dOtRiFN1bfbBSP2HcRel7MvmAY4PoBoypcNiFOb12jjgdA2iFVQD/IidTjrzKFtb79Mu5otLaV7/Cm1dnon+2o3/m449HkBRL0ZBhs7EqYGEQcy+6PbKZtC+4s/6R4DS8XYW7bHv2xJG4ACYP75QFPb94+IL09qatx6kOqRiEOUE4ZI865KC4HjL8UJbeZHuZExW5LyW3n9mWIiPQxwBEA/alFAOMQB2SuIAD6Z8tpdR5WaA3cNtvZqO+zM7xpj9fS7qDcWoFMJ6RpBiiNMKa1nuZZZgbflu08m0zw6Pyp8aSCW/Soan2NsKUV4OTBL/WaNYOex6f5PouhTv7ZaIU5eXsQP2uz1Tijs7ytVuOA/LUl9bblzy3fX7l8hjc7q2+saFN/xABXoqweRs21Cpdpe3rVA0C/Ggdoj3lTdxLq6Q+MGJ1pV6zOJlN40+o8zIS2tBCmCmry+9PCmSyYqYNY2rqq5xEiven7Jr8/rH+/KxDUXg7A5de4TxW2xODmQnroc3n8aeshkh7aXIikVfTSftNTjxc/B3l1ru8M4MxBLtsQByDjlyK9tgRohznAWlvSerxIq3qXzRchwP7KGw+dEiUxwPUjdh9KNQpxyW0aV+MA485HlMv0B5k6Gvm+6d1f9PBmIbjphjadwKZYR/ZYeUhTBzJB5/BqpmAHADgSSj6vRlhzqZ5HHvbE9V2JmBTqXPGoohIn//WVv6uCx6d4XwS3t+89SwU5l7o6J74vGYKc+kuUuk1kG+LU66qvfmL0pQjQb092tyX588v3Te9+u8KbnSeqsPqWWXIeOHsq7pwHrnAY4EqYXSczGDET4gDtua0A7Y4HMN/5iPQGXMtlGtOTqaNRr5NrZ2M1vNke3DKFNo3AJg9r8qCmDmdCNL1CaCrAifug8XiXbL4jlz8oPb/L65f2Sx3q9AKdmTCnF+QUh1jjUcXYOXc8WtQQByDjlyL5OkB27clqW5Lvj9462c7Hl/7c+vex+kbUhwGun7FahQPMndSQ6RAQAFNBTqSeU8zsgGs5vakKzMwzVujwlklWVTdpmX5406q06QU3eWjTDGtmT3zw+tMe7/IHNUOhUZATIr3JypxGiHPFY32HWOMRaSydPOQpwlosYmuIU7Ma4gDjL0WAfpCTryNfTy6f7SmbOfvsmHonm/DG6hv1ZwxwJS6bKpxdIQ6wfkgVMA5y6nXVtDojs9dRzCa4Afk5YUEtU/VNweIh07T7NU5gyCm8WT1jVb5+KjSJ2xQPlQrRKFw+X19IS+2bSz12TSPEiRQhzqK0EJeBuh2amX/R6jQjAExVtwH9MCeX77YEZD/ZMq+2QJQ7BrgyZDbEAeYOqQLpHQ+gHeREerP+W7notVYnY7SdbDobM1OFpG8jx57I65dCkOD29h0S9Pj6QprHnxbipPvdXinEufypSlYgCCHcKwUkIRaR7gOShzfFECcFKjHIyUOVmTCnMSWIfEyc+lCq9H+xQqYxNk7rDFSt8KYYM2cQ0LKe7kQm06FUILtxpkB6dRvQb092tCX1NjNtx8wXIcC+8MbqG1E6BjgHsLsKB+QW4rS2bxTkgPRqgFH4kndIRuup6XVY2VTd9B6XaTumeXyKKpw8qCU3bD7EiUEmWZGShTgASMQUIQ6AIshZlem3UPNMUyhDm3o9w+AGSK9FEc7k4S11+NQwvGX5eq3Sa1eKdTJ8KQJ02qHGFyMgcxuxsz3ZHdySz2O8Hxz3ln+ueNTGi9nbsx3KjAGuH7M7xAHmOh4g/XJNmcbsKPbbhtAm3xfFtot0iCehNQ+ZxRAHpA79iWO84lEpvMiDnJy4y7rBKlWZA1Jj0OSHb9WVOKQHsUzUz6sOjplCG5A5uKWtIw9v6kOyOnPE2SnTZL+AtS9FaY/NMJ5UzWx7stqWgNINb6y+UTlggHOIbM9ItTPEJfdDu+MBtIMcYBzm1PQ6I7OHg6x0NEAJjM/RCHGAbOybGEDMBjnoVMlklTtXcJBinJziUKqsSifdr1O9UoS9DBUu9fxwaYFSJ7Alf84c2oDMVTez4c3oig1qVq5FbHacKZB+BQi9L0aAtTFwRuvr7bMWs8HNaBsMb0S5YYBzkGKEOEAjgGl0POLzAPqdj9a20vfF+h9fo0OdVoKb3rasXts0nhA0OxHNKhzQFyLMBDkAiEUMx3DJn1mQjZUTPN6+M1bl4Q+pUCftaEy3YienqMyZWF97HJtBWAMUgU1zfROHSq1U3ayENyN2DFEASqstyfcr7blsDm9ElFnu114hR8j0bdbj0v+jqvtH2K39h9vrduk+n8/tSrtZYfbxRvtgR3jLVsLj0w8JHl9asBDcXukm8fr7bqp1BLcXgsenvPkHJoOQxw/BP6Dv5vEi4a9Q3ASPD4KvwtQNAwdLN1OP8fg0ns+bvKX2SdzP5L4OVLyOhK8i7bUq3gud9yPTeyz/bPQYfXHS/T2z0J7saktW2pPVtqRXddOrYudSeWP1rcAS8WRV3pab0YWczREEAWvXrsWIESNQUVGBhoYGvPfeexkft2HDBowdOxbBYBD19fXYtm2bdN9HH30El8uleXv00Udz3udiYAXOYXKZ3NfM4R6r1bjkPiX/1asiAOmVBK1t5ypjSLVYJcjlufSqcCIxLBhW5ADNqpycS6MFC/CnnS2aVtFTPw/EOdQKM9hf81qn4n161UW9Q7lGZ5RmqKjZVXHTffoshigA2bclcdu5yrYtZXr+fJ6wwPDWf9xyyy24/fbbcf/992PcuHG49tpr0djYiLfffhvBoHa1f8uWLWhqasLGjRtRX1+P1tZWNDY2YteuXRg+fDhGjx6NTz75RPGYTZs2Yf369TjzzDML8bJs5xKEDCNg+5lQKITq6mp0dnWhqqqq2LuTtVyu0GBmzE6mM+mMDt+oO59s9yETs3/s89HZmH9u852KZpjTY2JdzQvfq1md481uJs4ONTXlh4UgZjW0mWlrmX6fjdpTubclq/ug+xwOD3ChUAi1NTXo7u4uWN8k9oef7fhfVFUOtGebh3sweGpD1q9DEASMHDkSV155Jf77v/8bANDd3Y2amhps3rwZ5513nubj6uvrMWPGDNx5550AgEQigdGjR+Oyyy7D6tWrNR8zZcoUTJ06Fffcc4/l/SwFrMA5VCEqcYB+x6NXQUjuW+qxBp1PvqcGyHTZnlw7G7MyVeLk9IJFxiqdDkF1goQmf/YT4aY9nw1zq6XJoUJmR3XNbBvLdpwpYK66ndwX/efOp1zaEsDwVq5CoZDi50AggEAgkPFxH374ITo7O9HQ0CAtq66uRn19PTo6OjQDXCQSwfbt27FmzRppmdvtRkNDAzo6OjSfZ/v27di5cyc2bNhg9iWVHAY4B8s1xAGZv71n2/Ek96/v/2YqCbkyc63FYnQ24meUbSeTUxBRPdZSlc+A0SHQXOT7kKYZ+fpilGmOOKMvRUBh21Oh2hLA8FYKhGgvhIiF+ZsybAsARo8erVje3NyMdevWZXx8Z2cnAKCmpkaxvKamRrpP7eDBg4jH45qPeeeddzQfc8899+DEE0/E7NmzM+5TqWKAK3N2VeNEZsKcKJdOyOyFsUVmxgXlu1KgDgbF6HiiWoPmSog9XYg1uQxHsMqOtgTY256K1ZYAhrf+bO/evYpDqHrVt4ceegiXXHKJ9PNTTz2V9307evQoHn74YVx77bV5f658Ku2/5pRRLlU4kdm5rDJ1PkDmqpxie3k+B9rsYO5CdTZqhQwOIjvGS5E+K20p0zhTK20JyG97KsW2xPBW2qqqqkyNgTv77LNRX18v/RwOhwEAXV1dGDFihLS8q6sLdXV1mtsYOnQoPB4Purq6FMu7urpQW1ubtv5vf/tbHDlyBBdddJGZl1KyGOD6AbtCHGCug7cS5ADzHZAdrJyBZ2WsW6lfzieXYJYpSOSL1vtv9nWU8udh5xcigG1J8/lK+PMnayorK1FZWSn9LAgCamtr0d7eLgW2UCiErVu34tJLL9Xcht/vx7Rp09De3o6FCxcCSJ7E0N7ejpUrV6atf8899+Dss8/GsGHDbH89hcQA10+If9AKVY0DsuuAAHs7oWymTLB6kkKphQWrYc1qQMtnSJB/XhmvGWrwtuu9B6XyWeWjHQHav+/FbE/F+BLE8GY/IdwLwWfTGDjZVVyy4XK5sGrVKtxwww2YOHGiNI3IyJEjpXAGAPPmzcM555wjBbSmpiYsWbIE06dPx8yZM9Ha2oqenh4sW7ZMsf3du3fjxRdfxNNPP53TfpYCBrh+ptDVOMBaBwTYN++bVdmcXVoqgQCwFtzMfhaFrOhoPZ/R74L8NZj97MT3qBQ+t2zbEWAtdBejPRWres3wVh6uvvpq9PT04OKLL8ahQ4cwZ84ctLW1KeaAe//993Hw4EHp58WLF+PAgQNYu3YtOjs7UVdXh7a2trQTG+69914cd9xxmD9/fsFeT75wHrh+ys7xVfmu+ORTtlOClEIAENkd3KyEtlzPdrQyLsvOwfGAcz9DOae3JYY384o5D9y/X/wtqgbZNA/c5z049rT/LOjrKFeswPVTdlTiRIWqJNgll3ncSqnTLzY7pqoQt2EmyEUTQsYQFxeceR1Nq21IpH6thW5PpfAFqL8HN6JsMcD1Y3aNixNZGdcj7UOeOyA7O3OGt/yw8+xIJ4Y3uWyDnKgQga6UvgAxvBHpY4ArA/moxgHZdUKZOgd1h5TvDtsJoS2b4GwHj7v0DqGaVeqfa65BTmSmfRSiTTG4OZsQi0Kw6bJ6QsyeycIpMwa4MmF3NQ6wrxOSK1SFpdQ7eLVSm6tPK9hlW2krxTnGCiXXL0Rm5LNN5eO9ZngjMocBrsw4Jcjlg5M6di2lNFdfrodF8zHHWDl9vsXG4EZUfAxwZcrOw6qiUuyAnN6pa7E7yAH6gcqOaUbyPVdff/uM1a+nVNpTvt5nBjei7DDAlbF8VOOAwhwWMvPcxaLVIeXjsllW3metQJTtxLH5UIqTK6s/x2Jc+gwobqDL5/vM4FY6khezt+dsI/Fi9pR/DHCUtyAH5K/zKYWgBpjvhIzWs+N9z+Z9tnpCSS7sGIdV7IHyeusXOthpvQ9OalcMbkT2YIAjST6DnKhUglcu7O6A8lGxs6OTL+aUHcUOa9lsu1gVOsAZ7YrBjcheDHCUphQ6pFJUyA4oH9UeK518Pg7TFSpkFCsoyJ+XbacPgxtRfjDAkS4GuaRS6oCKMb6ulJXSZyPHtpNUqp8PKQnRKISoPfO32bUdyowBjjIqx87ISR1PvsfXFZOTPgct5dh2AOd/bkROwABHppVDZ9TfOp7+9nqcqhzaDsDfN6JCYoAjy/Ixh1yxseOhQuiv4+TYfogKjwGOstIfKgrsdKiY+kOYYxvqH4RIGILPpnngImFbtkOZ2fOJFcBHH32E7373uxg3bhwqKipwwgknoLm5GZGIPRfgpex43C5H/BEX91N+o8zc8WhONzLHab+fTthHov7OMRW4d955B4lEAnfffTcmTJiAN998E8uXL0dPTw9uvfXWYu9e2SuVihw7ldzYHbq0tpfw+Gx9jv6qVCYOlmP7IiodjglwCxYswIIFC6Sfx48fj127duGuu+5igCshhQxy7EzsUehKmfz5GOasK9RUMpmek4iKyzEBTkt3dzeGDBliuE44HEY43HdMPhQK5Xu3CPkJcuxE7FUKhzjd8ShDnA3ydd1Wtjmi0uXYALd7927ccccdGatvLS0tuO666wq0V6SW7UBtdhz5VQrhTcQQZ79cAh3bXhmKR4CYx75tUUEU/SSG1atXw+VyGd7eeecdxWP27duHBQsW4Nxzz8Xy5csNt79mzRp0d3dLt7179+bz5ZABrYHaejfKLwam8sK2R9T/FL0Cd+WVV2Lp0qWG64wfP176/8cff4wzzjgDs2fPxqZNmzJuPxAIIBAI5LqbRP1OwuMreiWOQZKIKDtFD3DDhg3DsGHDTK27b98+nHHGGZg2bRruu+8+uN1FLyASOZo8QBUqzDG0ERHlrugBzqx9+/Zh7ty5GDNmDG699VYcOHBAuq+2traIe0bUPzBYEZUnIRKG4LXn8Dkn8i0cxwS4Z599Frt378bu3btx3HHHKe4TBGfOYk5ERESUDcccg1y6dCkEQdC8EREREZUTxwQ4IiIiIkpyzCFUIiIisp8Q7oVg0zRwQrjXng1RRqzAERERETkMAxwRERGRwzDAERERETkMAxwRERGRwzDAERERlbFELGrrLVeCIGDt2rUYMWIEKioq0NDQgPfee8/wMS+++CK+/vWvY+TIkXC5XHjiiSfS1lm3bh0mTZqEgQMHYvDgwWhoaMDWrVtz3t9iYYAjIiKiknHLLbfg9ttvx8aNG7F161YMHDgQjY2N6O3VP8O1p6cHkydPxoYNG3TX+cIXvoA777wTb7zxBl566SWMHTsW8+fPV1zZyUk4jQgRERGVBEEQ0Nraip/85Cf4xje+AQB44IEHUFNTgyeeeALnnXee5uPOPPNMnHnmmYbb/q//+i/Fz7fddhvuuecevP7665g3b549L6CAWIEjIiIiW4VCIcUtHDZ3jdQPP/wQnZ2daGhokJZVV1ejvr4eHR0dtu1fJBLBpk2bUF1djcmTJ9u23UJiBY6IiKiMxcNRxN321HPi4eQYuNGjRyuWNzc3Y926dRkf39nZCQCoqalRLK+pqZHuy8WTTz6J8847D0eOHMGIESPw7LPPYujQoTlvtxhYgSMiIiJb7d27F93d3dJtzZo1mus99NBDGDRokHSLRnM/CcLIGWecgZ07d+Lvf/87FixYgG9961vYv39/Xp8zXxjgiIiIyFZVVVWKWyAQ0Fzv7LPPxs6dO6WbWA3r6upSrNfV1YXa2tqc92vgwIGYMGECvvKVr+Cee+6B1+vFPffck/N2i4GHUImIiKgoKisrUVlZKf0sCAJqa2vR3t6Ouro6AMnxdFu3bsWll15q+/MnEgnT4/NKDQMcERFRGUtEYkh47LmafSISy+nxLpcLq1atwg033ICJEydi3LhxuPbaazFy5EgsXLhQWm/evHk455xzsHLlSgDA559/jt27d0v3f/jhh9i5cyeGDBmC448/Hj09PfjZz36Gs88+GyNGjMDBgwexYcMG7Nu3D+eee25O+1wsDHBERERUMq6++mr09PTg4osvxqFDhzBnzhy0tbUhGAxK67z//vs4ePCg9POrr76KM844Q/q5qakJALBkyRJs3rwZHo8H77zzDu6//34cPHgQxx57LGbMmIG//vWv+NKXvlS4F2cjlyAIQrF3opBCoRCqq6vR2dWFqqqqYu8OERERQqEQamtq0N3dXbC+SewPP/r5KlRVaI9Rs7zNo2GMvbK1oK+jXPEkBiIiIiKH4SFUIiKiMhYPhxFz2bctKgxW4IiIiIgchgGOiIiIyGEY4IiIiIgchgGOiIiIyGF4EgMREVEZi/VGEYM9ZzHEevN7LVPqwwocERERkcMwwBERERE5DAMcERERkcNwDBwREVEZS0RiSLjtqefkejF7Mo8VOCIiIiKHYYAjIiIichgGOCIiIiKH4Rg4IiKiMhYPRxC3cVtUGKzAERERETkMAxwRERGRwzDAERERETkMx8ARERGVsXg0hrhN88DFo5wHrlBYgSMiIiJyGAY4IiIiIodhgCMiIiJyGAY4IiIiIofhSQxERERlLB6OIC4I9mwrErVlO5QZK3BEREREDsMAR0REROQwDHBEREREDsMxcERERGUsEYkhDpdt26LCYAWOiIiIyGEY4IiIiIgchgGOiIiIyGE4Bo6IiKiMxXujiCds2hbngSsYVuCIiIioZAiCgLVr12LEiBGoqKhAQ0MD3nvvPcPHvPjii/j617+OkSNHwuVy4Yknnkhbp6urC0uXLsXIkSMxYMAALFiwION2SxkDHBEREZWMW265Bbfffjs2btyIrVu3YuDAgWhsbERvb6/uY3p6ejB58mRs2LBB835BELBw4UJ88MEH+P3vf4/XXnsNY8aMQUNDA3p6evL1UvKKh1CJiIioJAiCgNbWVvzkJz/BN77xDQDAAw88gJqaGjzxxBM477zzNB935pln4swzz9Td7nvvvYeXX34Zb775Jr70pS8BAO666y7U1tbiN7/5Db73ve/Z/2LyjBU4IiIislUoFFLcwuGwqcd9+OGH6OzsRENDg7Ssuroa9fX16OjoyHp/xOcPBoPSMrfbjUAggJdeeinr7RYTAxwREVEZi4bjiPbG7LmF4wCA0aNHo7q6Wrq1tLSY2pfOzk4AQE1NjWJ5TU2NdF82Jk2ahOOPPx5r1qzBZ599hkgkgptvvhn/+te/8Mknn2S93WJigCMiIiJb7d27F93d3dJtzZo1mus99NBDGDRokHSLRvNzFqvP58Pvfvc7vPvuuxgyZAgGDBiAv/zlLzjzzDPhdjszCnEMHBEREdmqqqoKVVVVGdc7++yzUV9fL/0sHurs6urCiBEjpOVdXV2oq6vLaZ+mTZuGnTt3oru7G5FIBMOGDUN9fT2mT5+e03aLhQGOiIiIiqKyshKVlZXSz4IgoLa2Fu3t7VJgC4VC2Lp1Ky699FJbnrO6uhpA8sSGV199Fddff70t2y00BjgiIqIylojEkRBsuph9NJ7T410uF1atWoUbbrgBEydOxLhx43Dttddi5MiRWLhwobTevHnzcM4552DlypUAgM8//xy7d++W7v/www+xc+dODBkyBMcffzwA4NFHH8WwYcNw/PHH44033sDll1+OhQsXYv78+Tntc7EwwBEREVHJuPrqq9HT04OLL74Yhw4dwpw5c9DW1qY4g/T999/HwYMHpZ9fffVVnHHGGdLPTU1NAIAlS5Zg8+bNAIBPPvkETU1N0uHZiy66CNdee21hXlQeuARBEIq9E1aFw2HU19fjH//4B1577TVLx8VDoRCqq6vR2dVl6vg8ERFRvoVCIdTW1KC7u7tgfZPYH774f8/AIJ899ZzPozGc9vRfCvo6ypUjT724+uqrMXLkyGLvBhEREVFROO4Q6jPPPIM///nPeOyxx/DMM88Ue3eIiIgcLRqOIceha33bisXs2RBl5KgA19XVheXLl+OJJ57AgAEDTD0mHA4rZoAOhUL52j0iIiKignDMIVRBELB06VJ8//vftzRnS0tLi2I26NGjR+dxL4mIiIjyr+gBbvXq1XC5XIa3d955B3fccQcOHz6sO5uznjVr1ihmg967d2+eXgkRERFRYRT9EOqVV16JpUuXGq4zfvx4PPfcc+jo6EAgEFDcN336dFxwwQW4//77NR8bCATSHkNERERJiUgc8YRN88DFbBpMRxkVPcANGzYMw4YNy7je7bffjhtuuEH6+eOPP0ZjYyO2bNmiuAwHERERUX9X9ABnljiTsmjQoEEAgBNOOAHHHXdcMXaJiIiIqCiKPgaOiIiIiKxxTAVObezYsXDgRSSIiIiIcubYAEdERES5ix2NIWZTGohxIt+C4SFUIiIiIodhgCMiIiJyGAY4IiIiIofhGDgiIqIyFgvHYdfQtVicE/kWCitwRERERA7DAEdERETkMAxwRERERA7DMXBERERlLB5JIO6x52L28XjClu1QZqzAERERETkMAxwRERGRwzDAERERETkMx8ARERGVsVg4hphbsGdbCc4DVyiswBERERE5DAMcERERkcMwwBERERE5DAMcERERkcPwJAYiIqIylojEEbepnJPgSQwFwwocERERkcMwwBERERE5DAMcERERkcMwwBEREZWxSDRu6y1XgiBg7dq1GDFiBCoqKtDQ0ID33nvP8DEtLS2YMWMGKisrMXz4cCxcuBC7du1KW6+jowNf+9rXMHDgQFRVVeG0007D0aNHc97nYmCAIyIiopJxyy234Pbbb8fGjRuxdetWDBw4EI2Njejt7dV9zAsvvIAVK1bg5ZdfxrPPPotoNIr58+ejp6dHWqejowMLFizA/PnzsW3bNrzyyitYuXIl3G5nRiGXIAj2XD/DIUKhEKqrq9HZ1YWqqqpi7w4RERFCoRBqa2rQ3d1dsL5J7A9/PeyLGOD22LLNI4k4LjywK+vXIQgCRo4ciSuvvBL//d//DQDo7u5GTU0NNm/ejPPOO8/Udg4cOIDhw4fjhRdewGmnnQYA+MpXvoL/83/+D66//nrL+1WKnBk7iYiIqGSFQiHFLRwOm3rchx9+iM7OTjQ0NEjLqqurUV9fj46ODtPP393dDQAYMmQIAGD//v3YunUrhg8fjtmzZ6Ompgann346XnrpJQuvqrQwwBEREZWxo3HB1hsAjB49GtXV1dKtpaXF1L50dnYCAGpqahTLa2pqpPsySSQSWLVqFU499VScfPLJAIAPPvgAALBu3TosX74cbW1tmDp1KubNm5dxfF2pYoAjIiIiW+3duxfd3d3Sbc2aNZrrPfTQQxg0aJB0i0ajOT/3ihUr8Oabb+KRRx6RliUSCQDAJZdcgmXLlmHKlCn4xS9+gS9+8Yu49957c37OYuCVGIiIiMhWVVVVpsbAnX322aivr5d+Fg+1dnV1YcSIEdLyrq4u1NXVZdzeypUr8eSTT+LFF1/EcccdJy0Xt3XSSScp1j/xxBOxZ8+ejNstRazAERERUVFUVlZiwoQJ0u2kk05CbW0t2tvbpXVCoRC2bt2KWbNm6W5HEASsXLkSjz/+OJ577jmMGzdOcf/YsWMxcuTItKlF3n33XYwZM8beF1UgrMARERFRSXC5XFi1ahVuuOEGTJw4EePGjcO1116LkSNHYuHChdJ68+bNwznnnIOVK1cCSB42ffjhh/H73/8elZWV0ni56upqVFRUwOVy4aqrrkJzczMmT56Muro63H///XjnnXfw29/+thgvNWcMcERERGUsKgiIJOyZUSxqw8xkV199NXp6enDxxRfj0KFDmDNnDtra2hAMBqV13n//fRw8eFD6+a677gIAzJ07V7Gt++67D0uXLgUArFq1Cr29vbjiiivw6aefYvLkyXj22Wdxwgkn5LzPxcB54IiIiIqsmPPA/c/giahw2TMP3FEhjh989l5BX0e54hg4IiIiIodhgCMiIiJyGI6BIyIiKmNH4wLgsmc01dHyGpVVVKzAERERETkMAxwRERGRwzDAERERETkMx8ARERGVsWhCgMemMXB2zANH5rACR0REROQwDHBEREREDsMAR0REROQwHANHRERUxo7GBQiuhC3b6uUYuIJhBY6IiIjIYRjgiIiIiByGAY6IiIjIYRjgiIiIiByGJzEQERGVsUhCgNumiXwjPImhYFiBIyIiInIYBjgiIiIih2GAIyIiInIYjoEjIiIqY+GEANg0Bi7MMXAFwwocERERkcMwwBERERE5DAMcERERkcNwDBwREVEZOxpPIOGyZ1thIWHPhigjVuCIiIiIHIYBjoiIiMhhGOCIiIiIHIZj4IiIiMpYNCHAZdM8cFHOA1cwjqvAPfXUU6ivr0dFRQUGDx6MhQsXFnuXiIiIiArKURW4xx57DMuXL8eNN96Ir33ta4jFYnjzzTeLvVtEREREBeWYABeLxXD55Zdj/fr1+O53vystP+mkk4q4V0RERESF55gAt2PHDuzbtw9utxtTpkxBZ2cn6urqsH79epx88sm6jwuHwwiHw9LP3d3dAIDDhw/nfZ+JiIjMEPskoQhjyCJIADY9bQScB65QHBPgPvjgAwDAunXrcNttt2Hs2LH4+c9/jrlz5+Ldd9/FkCFDNB/X0tKC6667Lm35xAkT8rq/REREVv373/9GdXV1QZ7L7/ejtrYWD3Tus3W7tbW18Pv9tm6T0rmEYsR9mdWrV+Pmm282XOef//wnduzYgQsuuAB33303Lr74YgDJ6tpxxx2HG264AZdcconmY9UVuEOHDmHMmDHYs2dPwRqJE4RCIYwePRp79+5FVVVVsXenpPC90cb3RR/fG218X/R1d3fj+OOPx2effYZjjjmmYM/b29uLSCRi6zb9fj+CwaCt26R0Ra/AXXnllVi6dKnhOuPHj8cnn3wCQDnmLRAIYPz48dizZ4/uYwOBAAKBQNry6upq/gHRUFVVxfdFB98bbXxf9PG90cb3RZ/bXdjJIYLBIMOWQxU9wA0bNgzDhg3LuN60adMQCASwa9cuzJkzBwAQjUbx0UcfYcyYMfneTSIiIqKSUfQAZ1ZVVRW+//3vo7m5GaNHj8aYMWOwfv16AMC5555b5L0jIiIiKhzHBDgAWL9+PbxeL7797W/j6NGjqK+vx3PPPYfBgweb3kYgEEBzc7PmYdVyxvdFH98bbXxf9PG90cb3RR/fG7Kq6CcxEBEREZE1jruUFhEREVG5Y4AjIiIichgGOCIiIiKHYYAjIiIicpiyD3BPPfUU6uvrUVFRgcGDB2PhwoXF3qWSEg6HUVdXB5fLhZ07dxZ7d4rqo48+wne/+12MGzcOFRUVOOGEE9Dc3Gz7LOZOsWHDBowdOxbBYBD19fXYtm1bsXepqFpaWjBjxgxUVlZi+PDhWLhwIXbt2lXs3So5N910E1wuF1atWlXsXSkJ+/btw4UXXohjjz0WFRUV+PKXv4xXX3212LtFDlDWAe6xxx7Dt7/9bSxbtgz/+Mc/8Le//Q3/9V//VezdKilXX301Ro4cWezdKAnvvPMOEokE7r77brz11lv4xS9+gY0bN+LHP/5xsXet4LZs2YKmpiY0Nzdjx44dmDx5MhobG7F///5i71rRvPDCC1ixYgVefvllPPvss4hGo5g/fz56enqKvWsl45VXXsHdd9+NU045pdi7UhI+++wznHrqqfD5fHjmmWfw9ttv4+c//7mlqbGojAllKhqNCqNGjRJ+9atfFXtXStbTTz8tTJo0SXjrrbcEAMJrr71W7F0qObfccoswbty4Yu9Gwc2cOVNYsWKF9HM8HhdGjhwptLS0FHGvSsv+/fsFAMILL7xQ7F0pCYcPHxYmTpwoPPvss8Lpp58uXH755cXepaL70Y9+JMyZM6fYu0EOVbYVuB07dmDfvn1wu92YMmUKRowYgTPPPBNvvvlmsXetJHR1dWH58uV48MEHMWDAgGLvTsnq7u7GkCFDir0bBRWJRLB9+3Y0NDRIy9xuNxoaGtDR0VHEPSst3d3dAFB2vx96VqxYgbPOOkvxe1Pu/vCHP2D69Ok499xzMXz4cEyZMgW//OUvi71b5BBlG+A++OADAMC6devwk5/8BE8++SQGDx6MuXPn4tNPPy3y3hWXIAhYunQpvv/972P69OnF3p2StXv3btxxxx245JJLir0rBXXw4EHE43HU1NQoltfU1KCzs7NIe1VaEokEVq1ahVNPPRUnn3xysXen6B555BHs2LEDLS0txd6VkvLBBx/grrvuwsSJE/GnP/0Jl156KX74wx/i/vvvL/aukQP0uwC3evVquFwuw5s4lgkArrnmGixatAjTpk3DfffdB5fLhUcffbTIryI/zL43d9xxBw4fPow1a9YUe5cLwuz7Irdv3z4sWLAA5557LpYvX16kPadStWLFCrz55pt45JFHir0rRbd3715cfvnleOihhxAMBou9OyUlkUhg6tSpuPHGGzFlyhRcfPHFWL58OTZu3FjsXSMHcNS1UM248sorsXTpUsN1xo8fj08++QQAcNJJJ0nLA4EAxo8fjz179uRzF4vG7Hvz3HPPoaOjI+2afNOnT8cFF1zQ774dmn1fRB9//DHOOOMMzJ49G5s2bcrz3pWeoUOHwuPxoKurS7G8q6sLtbW1Rdqr0rFy5Uo8+eSTePHFF3HccccVe3eKbvv27di/fz+mTp0qLYvH43jxxRdx5513IhwOw+PxFHEPi2fEiBGKPggATjzxRDz22GNF2iNykn4X4IYNG4Zhw4ZlXG/atGkIBALYtWsX5syZAwCIRqP46KOPMGbMmHzvZlGYfW9uv/123HDDDdLPH3/8MRobG7FlyxbU19fncxeLwuz7AiQrb2eccYZUsXW7+10ROyO/349p06ahvb1dmnYnkUigvb0dK1euLO7OFZEgCLjsssvw+OOP4/nnn8e4ceOKvUslYd68eXjjjTcUy5YtW4ZJkybhRz/6UdmGNwA49dRT06aaeffdd/ttH0T26ncBzqyqqip8//vfR3NzM0aPHo0xY8Zg/fr1AIBzzz23yHtXXMcff7zi50GDBgEATjjhhLKuKOzbtw9z587FmDFjcOutt+LAgQPSfeVWeWpqasKSJUswffp0zJw5E62trejp6cGyZcuKvWtFs2LFCjz88MP4/e9/j8rKSmk8YHV1NSoqKoq8d8VTWVmZNg5w4MCBOPbYY8t+fOAVV1yB2bNn48Ybb8S3vvUtbNu2DZs2bSrLyj5ZV7YBDgDWr18Pr9eLb3/72zh69Cjq6+vx3HPPcQ4e0vTss89i9+7d2L17d1qQFQShSHtVHIsXL8aBAwewdu1adHZ2oq6uDm1tbWknNpSTu+66CwAwd+5cxfL77rsv4yF6Kk8zZszA448/jjVr1uCnP/0pxo0bh9bWVlxwwQXF3jVyAJdQbj0PERERkcOV3wAeIiIiIodjgCMiIiJyGAY4IiIiIodhgCMiIiJyGAY4IiIiIodhgCMiIiJyGAY4IiIiIodhgCMiIiJyGAY4IiIiIodhgCMiIiJyGAY4IiIiIodhgCMiS37zm9+goqICn3zyibRs2bJlOOWUU9Dd3V3EPSMiKh+8mD0RWSIIAurq6nDaaafhjjvuQHNzM+699168/PLLGDVqVLF3j4ioLHiLvQNE5Cwulws/+9nP8J//+Z+ora3FHXfcgb/+9a8Mb0REBcQKHBFlZerUqXjrrbfw5z//Gaeffnqxd4eIqKxwDBwRWdbW1oZ33nkH8XgcNTU1xd4dIqKywwocEVmyY8cOzJ07F3fffTc2b96MqqoqPProo8XeLSKissIxcERk2kcffYSzzjoLP/7xj3H++edj/PjxmDVrFnbs2IGpU6cWe/eIiMoGK3BEZMqnn36K2bNnY+7cudi4caO0/KyzzkI8HkdbW1sR946IqLwwwBERERE5DE9iICIiInIYBjgiIiIih2GAIyIiInIYBjgiIiIih2GAIyIiInIYBjgiIiIih2GAIyIiInIYBjgiIiIih2GAIyIiInIYBjgiIiIih2GAIyIiInKY/w97zHplNe42vwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "id": "e7bd6410-395b-497d-ad66-154f692bbabc",
    "metadata": {
     "execution": {
@@ -164,14 +164,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "qumodes:  0  qubits:  0     with amplitude: 0.149 - i0.000 (big endian)\n",
-      "qumodes:  2  qubits:  0     with amplitude: 0.474 - i0.000 (big endian)\n",
-      "qumodes:  4  qubits:  0     with amplitude: 0.616 - i0.000 (big endian)\n",
-      "qumodes:  6  qubits:  0     with amplitude: 0.506 - i0.000 (big endian)\n",
-      "qumodes:  8  qubits:  0     with amplitude: 0.304 - i0.000 (big endian)\n",
-      "qumodes:  10  qubits:  0     with amplitude: 0.144 - i0.000 (big endian)\n",
-      "qumodes:  12  qubits:  0     with amplitude: 0.056 - i0.000 (big endian)\n",
-      "qumodes:  14  qubits:  0     with amplitude: 0.018 - i0.000 (big endian)\n"
+      "qumodes:  1  qubits:  1     with amplitude: 0.316 - i0.000 (big endian)\n",
+      "qumodes:  3  qubits:  1     with amplitude: 0.581 - i0.000 (big endian)\n",
+      "qumodes:  5  qubits:  1     with amplitude: 0.585 - i0.000 (big endian)\n",
+      "qumodes:  7  qubits:  1     with amplitude: 0.406 - i0.000 (big endian)\n",
+      "qumodes:  9  qubits:  1     with amplitude: 0.215 - i0.000 (big endian)\n",
+      "qumodes:  11  qubits:  1     with amplitude: 0.092 - i0.000 (big endian)\n",
+      "qumodes:  13  qubits:  1     with amplitude: 0.034 - i0.000 (big endian)\n",
+      "qumodes:  15  qubits:  1     with amplitude: 0.013 - i0.000 (big endian)\n"
      ]
     }
    ],
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 28,
    "id": "4c2f5dad-69ae-4f26-9f51-2c249b5746cc",
    "metadata": {
     "execution": {
@@ -202,7 +202,16 @@
      "shell.execute_reply": "2023-11-16T20:47:16.840857Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\stav405\\git\\bosonic-qiskit\\venv\\Lib\\site-packages\\qiskit\\circuit\\quantumcircuit.py:2527: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "  base_instruction = CircuitInstruction(operation, (), ())\n"
+     ]
+    }
+   ],
    "source": [
     "# Let's first import the GKP parameters\n",
     "GKP_params = np.load('GKP_params.npz')\n",
@@ -226,7 +235,12 @@
     "# see https://www.nature.com/articles/s41567-022-01776-9.\n",
     "for beta, phi, theta in zip(GKP_params['betas'], GKP_params['phis'], GKP_params['thetas']):\n",
     "    circuit.append(RGate(theta,phi),[qbr[0]])\n",
+    "    \n",
+    "    # FIXME -- Upgrade to NumPy v2.0.0+ causes Qiskit validation error when passing numpy.complex128 beta/2 type\n",
+    "    #   Qiskit v1.2.4 and NumPy <2.0.0 cause ComplexWarning: Casting complex values to real discards the imaginary part base_instruction = CircuitInstruction(operation, (), ())\n",
+    "    #   Qiskit <=1.0.1 seemed to just ignore it and keep going (no warning given)\n",
     "    circuit.cv_c_d(beta/2,qmr[0],qbr[0])\n",
+    "    \n",
     "    circuit.x(qbr[0])\n",
     "    \n",
     "# Now draw the circuit using Qiskit's draw() function\n",
@@ -235,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 29,
    "id": "92bed7bc-5ebf-4403-b065-67da6ece48aa",
    "metadata": {
     "execution": {
@@ -248,7 +262,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -278,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 30,
    "id": "a78b7360-379f-4a39-9e6e-f73a34cc1d21",
    "metadata": {
     "execution": {
@@ -316,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 31,
    "id": "a49ffd10-bd23-41ec-843c-d05356dcbcc9",
    "metadata": {
     "execution": {
@@ -387,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 32,
    "id": "3fae7717-0698-45c0-93b2-e737d95fa383",
    "metadata": {
     "execution": {
@@ -409,7 +423,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 400x800 with 2 Axes>"
       ]
@@ -459,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "id": "6e7a4dc4-392f-472d-b62d-4fb591d09730",
    "metadata": {
     "execution": {
@@ -474,27 +488,29 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\stav405\\git\\bosonic-qiskit\\c2qa\\kraus.py:185: UserWarning: PhotonLossNoisePass ignores instructions without duration, you may need to schedule circuit in advance.\n",
+      "c:\\Users\\stav405\\git\\bosonic-qiskit\\c2qa\\kraus.py:197: UserWarning: PhotonLossNoisePass ignores instructions without duration, you may need to schedule circuit in advance.\n",
       "  warnings.warn(\n",
-      "c:\\Users\\stav405\\git\\bosonic-qiskit\\venv\\Lib\\site-packages\\scipy\\sparse\\_index.py:100: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
-      "  self._set_intXint(row, col, x.flat[0])\n"
+      "c:\\Users\\stav405\\git\\bosonic-qiskit\\venv\\Lib\\site-packages\\scipy\\sparse\\_index.py:108: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil and dok are more efficient.\n",
+      "  self._set_intXint(row, col, x.flat[0])\n",
+      "c:\\Users\\stav405\\git\\bosonic-qiskit\\c2qa\\kraus.py:197: UserWarning: PhotonLossNoisePass ignores instructions without duration, you may need to schedule circuit in advance.\n",
+      "  warnings.warn(\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.animation.FuncAnimation at 0x133c5d7b3d0>"
+       "'<video width=\"640\" height=\"480\" controls autoplay loop>\\n  <source type=\"video/mp4\" 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YN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       ]
      },
-     "execution_count": 26,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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YGIj29vZobGw8av4ll1wSL7/8cnR2dg4+PvvZz8Z1110XnZ2d3l4GADjNlXVnMSKipaUlFi1aFLNnz445c+bEmjVr4tChQ7F48eKIiFi4cGFMmzYt2traorq6Oi699NIh688777yIiKPGAQA4/ZQdi/Pnz4/9+/fHypUro6urK2bOnBlbtmwZ/NDL3r17o6LCD4YBADgTjCuKojjVmzie3t7eqK2tjZ6enqipqTnV2wEAOC2NRjO5BQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBpRLK5duzamT58e1dXV0dDQENu2bUvnrl+/Pq655pqYMGFCTJgwIZqamo45HwCA00fZsbhx48ZoaWmJ1tbW2LFjR8yYMSOam5vjrbfeGnb+1q1b48Ybb4wf/ehH0dHREfX19fGZz3wmfvnLX37gzQMAMLrGFUVRlLOgoaEhrrzyynjkkUciImJgYCDq6+vjjjvuiGXLlh13fX9/f0yYMCEeeeSRWLhw4Qlds7e3N2pra6OnpydqamrK2S4AwB+N0Wimsu4s9vX1xfbt26Opqen3T1BREU1NTdHR0XFCz/HOO+/Eu+++G+eff3465/Dhw9Hb2zvkAQDA2CsrFg8cOBD9/f1RV1c3ZLyuri66urpO6DnuuuuumDp16pDgfL+2traora0dfNTX15ezTQAATpIx/TT06tWrY8OGDfHss89GdXV1Om/58uXR09Mz+Ni3b98Y7hIAgPeML2fyxIkTo7KyMrq7u4eMd3d3x+TJk4+59sEHH4zVq1fHD3/4w7j88suPObdUKkWpVCpnawAAjIKy7ixWVVXFrFmzor29fXBsYGAg2tvbo7GxMV33wAMPxP333x9btmyJ2bNnj3y3AACMqbLuLEZEtLS0xKJFi2L27NkxZ86cWLNmTRw6dCgWL14cERELFy6MadOmRVtbW0RE/PM//3OsXLkynnrqqZg+ffrg32380Ic+FB/60IdO4ksBAOBkKzsW58+fH/v374+VK1dGV1dXzJw5M7Zs2TL4oZe9e/dGRcXvb1h+85vfjL6+vvibv/mbIc/T2toaX/nKVz7Y7gEAGFVlf8/iqeB7FgEAju+Uf88iAAB/XMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKTEIgAAKbEIAEBKLAIAkBKLAACkxCIAACmxCABASiwCAJASiwAApMQiAAApsQgAQEosAgCQEosAAKRGFItr166N6dOnR3V1dTQ0NMS2bduOOf973/teXHLJJVFdXR2XXXZZbN68eUSbBQBgbJUdixs3boyWlpZobW2NHTt2xIwZM6K5uTneeuutYee/9NJLceONN8bNN98cO3fujHnz5sW8efPiZz/72QfePAAAo2tcURRFOQsaGhriyiuvjEceeSQiIgYGBqK+vj7uuOOOWLZs2VHz58+fH4cOHYof/OAHg2N/+Zd/GTNnzox169ad0DV7e3ujtrY2enp6oqamppztAgD80RiNZhpfzuS+vr7Yvn17LF++fHCsoqIimpqaoqOjY9g1HR0d0dLSMmSsubk5nnvuufQ6hw8fjsOHDw/+uqenJyJ+9y8AAIDhvddKZd4LPKayYvHAgQPR398fdXV1Q8br6upi9+7dw67p6uoadn5XV1d6nba2trjvvvuOGq+vry9nuwAAf5T+53/+J2pra0/Kc5UVi2Nl+fLlQ+5Gvv322/HhD3849u7de9JeOKdeb29v1NfXx759+/z1gjOEMz0zOdczk3M9M/X09MQFF1wQ559//kl7zrJiceLEiVFZWRnd3d1Dxru7u2Py5MnDrpk8eXJZ8yMiSqVSlEqlo8Zra2v9hj4D1dTUONczjDM9MznXM5NzPTNVVJy8b0cs65mqqqpi1qxZ0d7ePjg2MDAQ7e3t0djYOOyaxsbGIfMjIl544YV0PgAAp4+y34ZuaWmJRYsWxezZs2POnDmxZs2aOHToUCxevDgiIhYuXBjTpk2Ltra2iIi4884749prr42HHnoobrjhhtiwYUP89Kc/jccee+zkvhIAAE66smNx/vz5sX///li5cmV0dXXFzJkzY8uWLYMfYtm7d++QW59XXXVVPPXUU3HPPffE3XffHX/xF38Rzz33XFx66aUnfM1SqRStra3DvjXNHy7neuZxpmcm53pmcq5nptE417K/ZxEAgD8efjY0AAApsQgAQEosAgCQEosAAKTEIgAAqdMmFteuXRvTp0+P6urqaGhoiG3bth1z/ve+97245JJLorq6Oi677LLYvHnzGO2UE1XOma5fvz6uueaamDBhQkyYMCGampqO+3uAU6PcP6vv2bBhQ4wbNy7mzZs3uhtkRMo917fffjuWLFkSU6ZMiVKpFBdffLH/Dp+Gyj3XNWvWxEc/+tE4++yzo76+PpYuXRq//e1vx2i3HM+Pf/zjmDt3bkydOjXGjRsXzz333HHXbN26NT75yU9GqVSKj3zkI/Hkk0+Wf+HiNLBhw4aiqqqqeOKJJ4r/+q//Km699dbivPPOK7q7u4ed/5Of/KSorKwsHnjggeKVV14p7rnnnuKss84qXn755THeOZlyz/Smm24q1q5dW+zcubPYtWtX8Xd/93dFbW1t8d///d9jvHOOpdxzfc8bb7xRTJs2rbjmmmuKv/7rvx6bzXLCyj3Xw4cPF7Nnzy6uv/764sUXXyzeeOONYuvWrUVnZ+cY75xjKfdcv/Od7xSlUqn4zne+U7zxxhvF888/X0yZMqVYunTpGO+czObNm4sVK1YUzzzzTBERxbPPPnvM+Xv27CnOOeecoqWlpXjllVeKb3zjG0VlZWWxZcuWsq57WsTinDlziiVLlgz+ur+/v5g6dWrR1tY27PzPfe5zxQ033DBkrKGhofj7v//7Ud0nJ67cM32/I0eOFOeee27x7W9/e7S2yAiM5FyPHDlSXHXVVcW3vvWtYtGiRWLxNFTuuX7zm98sLrzwwqKvr2+stsgIlHuuS5YsKT796U8PGWtpaSmuvvrqUd0nI3MisfjlL3+5+MQnPjFkbP78+UVzc3NZ1zrlb0P39fXF9u3bo6mpaXCsoqIimpqaoqOjY9g1HR0dQ+ZHRDQ3N6fzGVsjOdP3e+edd+Ldd9+N888/f7S2SZlGeq5f/epXY9KkSXHzzTePxTYp00jO9fvf/340NjbGkiVLoq6uLi699NJYtWpV9Pf3j9W2OY6RnOtVV10V27dvH3yres+ePbF58+a4/vrrx2TPnHwnq5fK/nF/J9uBAweiv79/8McFvqeuri5279497Jqurq5h53d1dY3aPjlxIznT97vrrrti6tSpR/0m59QZybm++OKL8fjjj0dnZ+cY7JCRGMm57tmzJ/7jP/4jPv/5z8fmzZvj9ddfjy9+8Yvx7rvvRmtr61hsm+MYybnedNNNceDAgfjUpz4VRVHEkSNH4vbbb4+77757LLbMKMh6qbe3N37zm9/E2WeffULPc8rvLML7rV69OjZs2BDPPvtsVFdXn+rtMEIHDx6MBQsWxPr162PixImnejucRAMDAzFp0qR47LHHYtasWTF//vxYsWJFrFu37lRvjQ9g69atsWrVqnj00Udjx44d8cwzz8SmTZvi/vvvP9Vb4xQ75XcWJ06cGJWVldHd3T1kvLu7OyZPnjzsmsmTJ5c1n7E1kjN9z4MPPhirV6+OH/7wh3H55ZeP5jYpU7nn+vOf/zzefPPNmDt37uDYwMBARESMHz8+Xn311bjoootGd9Mc10j+vE6ZMiXOOuusqKysHBz72Mc+Fl1dXdHX1xdVVVWjumeObyTneu+998aCBQvilltuiYiIyy67LA4dOhS33XZbrFixIioq3F/6Q5P1Uk1NzQnfVYw4De4sVlVVxaxZs6K9vX1wbGBgINrb26OxsXHYNY2NjUPmR0S88MIL6XzG1kjONCLigQceiPvvvz+2bNkSs2fPHoutUoZyz/WSSy6Jl19+OTo7Owcfn/3sZ+O6666Lzs7OqK+vH8vtkxjJn9err746Xn/99cH4j4h47bXXYsqUKULxNDGSc33nnXeOCsL3/g/B7z5PwR+ak9ZL5X32ZnRs2LChKJVKxZNPPlm88sorxW233Vacd955RVdXV1EURbFgwYJi2bJlg/N/8pOfFOPHjy8efPDBYteuXUVra6uvzjnNlHumq1evLqqqqoqnn366+NWvfjX4OHjw4Kl6CQyj3HN9P5+GPj2Ve6579+4tzj333OIf/uEfildffbX4wQ9+UEyaNKn42te+dqpeAsMo91xbW1uLc889t/i3f/u3Ys+ePcW///u/FxdddFHxuc997lS9BN7n4MGDxc6dO4udO3cWEVE8/PDDxc6dO4tf/OIXRVEUxbJly4oFCxYMzn/vq3P+6Z/+qdi1a1exdu3aP9yvzimKovjGN75RXHDBBUVVVVUxZ86c4j//8z8H/9m1115bLFq0aMj87373u8XFF19cVFVVFZ/4xCeKTZs2jfGOOZ5yzvTDH/5wERFHPVpbW8d+4xxTuX9W/39i8fRV7rm+9NJLRUNDQ1EqlYoLL7yw+PrXv14cOXJkjHfN8ZRzru+++27xla98pbjooouK6urqor6+vvjiF79Y/O///u/Yb5xh/ehHPxr2fyvfO8dFixYV11577VFrZs6cWVRVVRUXXnhh8a//+q9lX3dcUbi3DADA8E7531kEAOD0JRYBAEiJRQAAUmIRAICUWAQAICUWAQBIiUUAAFJiEQCAlFgEACAlFgEASIlFAABS/w82LIAY6w4wsQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -524,19 +540,18 @@
     "state, result, _ = c2qa.util.simulate(circuit, noise_passes=noise_pass)\n",
     "\n",
     "# Alternatively, we can animate our circuit using the animate_wigner() function built-into bosonic qiskit.\n",
-    "wigner_filename = \"figures/photon_loss.gif\"\n",
-    "c2qa.animate.animate_wigner(\n",
+    "anim = c2qa.animate.animate_wigner(\n",
     "    circuit,\n",
     "    animation_segments=200,\n",
-    "    # file=wigner_filename,\n",
     "    noise_passes=noise_pass\n",
-    ")   "
+    ")   \n",
+    "anim.to_html5_video()"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -550,7 +565,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.1"
   }
  },
  "nbformat": 4,


Test patch:
diff --git a/tests/test_animate.py b/tests/test_animate.py
index 9390b20..e9a713f 100644
--- a/tests/test_animate.py
+++ b/tests/test_animate.py
@@ -22,25 +22,27 @@ def __build_subcircuit():
     alpha = 1
 
     # Choose total animation time
-    total_time = 1*2*numpy.pi/omega_R
+    total_time = 1 * 2 * numpy.pi / omega_R
 
     # Create new circuit
-    qmr = c2qa.QumodeRegister(num_qumodes=1, num_qubits_per_qumode=num_qubits_per_qumode)
+    qmr = c2qa.QumodeRegister(
+        num_qumodes=1, num_qubits_per_qumode=num_qubits_per_qumode
+    )
     qbr = qiskit.QuantumRegister(1)
-    U_JC = c2qa.CVCircuit(qmr,qbr)
+    U_JC = c2qa.CVCircuit(qmr, qbr)
 
     # Append U_R
-    U_JC.cv_r(-omega_R*total_time, qmr[0])
+    U_JC.cv_r(-omega_R * total_time, qmr[0])
     # Append U_Q
-    U_JC.rz(omega_Q*total_time,qbr[0])
+    U_JC.rz(omega_Q * total_time, qbr[0])
     # Append U_\chi -- KS: this needs to be updated to reflect naming conventions in manuscript
-    U_JC.cv_c_r(-chi*total_time/2, qmr[0], qbr[0])
+    U_JC.cv_c_r(-chi * total_time / 2, qmr[0], qbr[0])
     # Compile this circuit into a single parameterized gate
-    U_JC = U_JC.to_gate(label='U_JC')
+    U_JC = U_JC.to_gate(label="U_JC")
 
     # Instantiate the circuit and initialize the qubit to the '0' state.
-    circuit_0 = c2qa.CVCircuit(qmr,qbr)
-    circuit_0.initialize([1,0], qbr)
+    circuit_0 = c2qa.CVCircuit(qmr, qbr)
+    circuit_0.initialize([1, 0], qbr)
 
     # Squeeze so we can visually see rotation
     circuit_0.cv_sq(0.5, qmr[0])
@@ -50,12 +52,10 @@ def __build_subcircuit():
     # coeffs = [numpy.exp(-numpy.abs(alpha)**2/2)*alpha**n/(numpy.sqrt(numpy.math.factorial(n))) for n in range(0,cutoff)]
     # circuit_0.cv_initialize(coeffs,qmr[0])
 
-
     # Append time evolution unitary
-    circuit_0.append(U_JC,qmr[0] + [qbr[0]])
+    circuit_0.append(U_JC, qmr[0] + [qbr[0]])
     # circuit_0.assign_parameters({dt : total_time})
 
-
     # dt = total_time
     # # Append U_R
     # circuit_0.cv_r(-omega_R*dt,qmr[0])
@@ -67,8 +67,6 @@ def __build_subcircuit():
     # Compile this circuit into a single parameterized gate
     # U_JC = U_JC.to_gate(label='U_JC')
 
-
-
     # # Now repeat the above steps for a qubit initialized to the '1' state:
     # circuit_1 = c2qa.CVCircuit(qmr,qbr)
     # circuit_1.initialize([0,1], qbr)
@@ -80,25 +78,32 @@ def __build_subcircuit():
 
 
 def test_animate_subcircuit_one_gate(capsys):
-    """ Test animating a circuit with a composite gate built from another circuit.
-    Composite gate borrowed from Jaynes-Cummings model tutorial """
+    """Test animating a circuit with a composite gate built from another circuit.
+    Composite gate borrowed from Jaynes-Cummings model tutorial"""
 
     with capsys.disabled():
         circuit = __build_subcircuit()
 
         # Animate wigner function of each circuit
-        c2qa.animate.animate_wigner(circuit,file="tests/composite_gate.gif", animation_segments = 20)
+        c2qa.animate.animate_wigner(
+            circuit, file="tests/composite_gate.gif", animation_segments=20
+        )
 
 
 def test_animate_subcircuit_sequential(capsys):
-    """ Test animating a circuit with a composite gate built from another circuit.
-    Composite gate borrowed from Jaynes-Cummings model tutorial """
+    """Test animating a circuit with a composite gate built from another circuit.
+    Composite gate borrowed from Jaynes-Cummings model tutorial"""
 
     with capsys.disabled():
         circuit = __build_subcircuit()
 
         # Animate wigner function of each circuit
-        c2qa.animate.animate_wigner(circuit,file="tests/sequential_subcircuit.gif", animation_segments = 20, sequential_subcircuit = True)
+        c2qa.animate.animate_wigner(
+            circuit,
+            file="tests/sequential_subcircuit.gif",
+            animation_segments=20,
+            sequential_subcircuit=True,
+        )
 
 
 def test_animate_parameterized(capsys):
@@ -116,7 +121,7 @@ def test_animate_parameterized(capsys):
         minimal_circuit.cv_c_d(1j * a, qmr[0], qbr[0])
 
         bound_circuit = minimal_circuit.assign_parameters({a: 2})
-        
+
         wigner_filename = "tests/animate_parameterized.apng"
         c2qa.animate.animate_wigner(
             bound_circuit,
@@ -234,4 +239,4 @@ def test_calibration_animate_mp4(capsys):
             axes_max=8,
             animation_segments=48,
             shots=128,
-        )
\ No newline at end of file
+        )
diff --git a/tests/test_circuit.py b/tests/test_circuit.py
index 2ab542a..eec6b12 100644
--- a/tests/test_circuit.py
+++ b/tests/test_circuit.py
@@ -85,7 +85,7 @@ def test_get_qubit_indices(capsys):
         )
         qbr = qiskit.QuantumRegister(size=number_of_qubits)
         cbr = qiskit.ClassicalRegister(size=1)
-        circuit = c2qa.CVCircuit(qmr, qbr, cbr)  
+        circuit = c2qa.CVCircuit(qmr, qbr, cbr)
 
         indices = circuit.get_qubit_indices([qmr[1]])
         print(f"qmr[1] indices = {indices}")
@@ -103,6 +103,7 @@ def test_get_qubit_indices(capsys):
         print(f"qbr[0] indices = {indices}")
         assert indices == [4]
 
+
 def test_initialize_qubit_values(capsys):
     with capsys.disabled():
         print()
@@ -112,7 +113,8 @@ def test_initialize_qubit_values(capsys):
 
         for fock in range(pow(2, number_of_qubits_per_mode)):
             qmr = c2qa.QumodeRegister(
-                num_qumodes=number_of_modes, num_qubits_per_qumode=number_of_qubits_per_mode
+                num_qumodes=number_of_modes,
+                num_qubits_per_qumode=number_of_qubits_per_mode,
             )
             circuit = c2qa.CVCircuit(qmr)
             circuit.cv_initialize(fock, qmr[0])
@@ -130,15 +132,15 @@ def test_serialize(capsys):
         qumodes = c2qa.QumodeRegister(2)
         bosonic_circuit = c2qa.CVCircuit(qumodes)
 
-        init_state = [0,2]
+        init_state = [0, 2]
         for i in range(qumodes.num_qumodes):
             bosonic_circuit.cv_initialize(init_state[i], qumodes[i])
 
-        phi = qiskit.circuit.Parameter('phi')
-        bosonic_circuit.cv_bs(phi, qumodes[0], qumodes[1], qumodes.cutoff, qumodes.cutoff)
+        phi = qiskit.circuit.Parameter("phi")
+        bosonic_circuit.cv_bs(phi, qumodes[0], qumodes[1])
 
-        #print(bosonic_circuit.draw())
-        print('\nAttempt to serialize an unbound CVCircuit:')
+        # print(bosonic_circuit.draw())
+        print("\nAttempt to serialize an unbound CVCircuit:")
         bosonic_serial = json.dumps(bosonic_circuit, cls=RuntimeEncoder)
         print(bosonic_serial)
 
@@ -146,16 +148,22 @@ def test_serialize(capsys):
 def test_cv_gate_from_matrix(capsys):
     with capsys.disabled():
         # This test picks random qumode/qubit to initialize xgate on, does fockcounts, and checks that results match expected values.
-        xgate = [[0, 1],[1, 0]] # For qubit, flips |1> to |0> and vice versa. For qumode, flips fock |1> to fock |0> and vice versa
-        
+        xgate = [
+            [0, 1],
+            [1, 0],
+        ]  # For qubit, flips |1> to |0> and vice versa. For qumode, flips fock |1> to fock |0> and vice versa
+
         num_qumode_registers = 2
         num_qumodes_per_register = 2
         num_qubits_per_qumode = 1
 
         num_qubits = 3
 
-        total_qubits = num_qumode_registers * num_qumodes_per_register * num_qubits_per_qumode + num_qubits
-        for i in range(10): # Repeat test 10 times
+        total_qubits = (
+            num_qumode_registers * num_qumodes_per_register * num_qubits_per_qumode
+            + num_qubits
+        )
+        for i in range(10):  # Repeat test 10 times
             # Two qumode registers, One quantum register, One classical register. Hilbert space dimension = 2**(2 * 2 * 1 + 3) = 128
             qmr1 = c2qa.QumodeRegister(num_qumodes_per_register, num_qubits_per_qumode)
             qmr2 = c2qa.QumodeRegister(num_qumodes_per_register, num_qubits_per_qumode)
@@ -169,7 +177,7 @@ def test_cv_gate_from_matrix(capsys):
 
             for i in range(num_qubits):
                 circuit.initialize([0, 1], q[i])
-            
+
             # Pick one of the qumode registers to act on
             register = numpy.random.randint(1, 3)
 
@@ -180,16 +188,16 @@ def test_cv_gate_from_matrix(capsys):
             qubit_no = numpy.random.randint(0, 3)
 
             # Create string corresponding to expected results
-            expect = ['1' for _ in range(total_qubits)]
+            expect = ["1" for _ in range(total_qubits)]
 
             if register == 1:
-                expect[qumode_no] = '0'
+                expect[qumode_no] = "0"
             elif register == 2:
-                expect[qumode_no + 2] = '0'
+                expect[qumode_no + 2] = "0"
 
-            expect[4 + qubit_no] = '0' 
+            expect[4 + qubit_no] = "0"
 
-            expect = ''.join(reversed(expect))
+            expect = "".join(reversed(expect))
 
             # Depending on quantum register chosen, assert result to be true
             if register == 1:
@@ -204,7 +212,7 @@ def test_cv_gate_from_matrix(capsys):
                 if len(list(fock_counts.keys())) > 1:
                     raise Exception
 
-                assert(list(fock_counts.keys())[0] == expect)
+                assert list(fock_counts.keys())[0] == expect
 
             elif register == 2:
                 circuit.cv_gate_from_matrix(xgate, qmr2[qumode_no])
@@ -218,7 +226,7 @@ def test_cv_gate_from_matrix(capsys):
                 if len(list(fock_counts.keys())) > 1:
                     raise Exception
 
-                assert(list(fock_counts.keys())[0] == expect)
+                assert list(fock_counts.keys())[0] == expect
 
             else:
                 raise Exception
@@ -227,7 +235,7 @@ def test_cv_gate_from_matrix(capsys):
 def test_discretize_cv_gate_from_matrix(capsys):
     with capsys.disabled():
         # This test picks random qumode/qubit to initialize xgate on, does fockcounts, and checks that results match expected values.
-        xgate = [[0, 1],[1, 0]]
+        xgate = [[0, 1], [1, 0]]
 
         num_qumode_registers = 2
         num_qumodes_per_register = 2
@@ -235,7 +243,10 @@ def test_discretize_cv_gate_from_matrix(capsys):
 
         num_qubits = 3
 
-        total_qubits = num_qumode_registers * num_qumodes_per_register * num_qubits_per_qumode + num_qubits
+        total_qubits = (
+            num_qumode_registers * num_qumodes_per_register * num_qubits_per_qumode
+            + num_qubits
+        )
 
         # Two qumode registers, One quantum register, One classical register. Hilbert space dimension = 2**(2 * 2 * 1 + 3) = 128
         qmr1 = c2qa.QumodeRegister(num_qumodes_per_register, num_qubits_per_qumode)
@@ -251,11 +262,12 @@ def test_discretize_cv_gate_from_matrix(capsys):
         discretized = c2qa.discretize.discretize_circuits(circuit)
         assert len(circuit.data) == len(discretized)
 
+
 def test_cv_initialize(capsys):
     with capsys.disabled():
         qmr1 = c2qa.QumodeRegister(1, 2)
         qmr2 = c2qa.QumodeRegister(2, 2)
-        qmr3 = c2qa.QumodeRegister(2, 3) # <----- change to (2, 2) for no error
+        qmr3 = c2qa.QumodeRegister(2, 3)  # <----- change to (2, 2) for no error
         qmr4 = c2qa.QumodeRegister(1, 2)
         qmr5 = c2qa.QumodeRegister(3, 2)
         qmr6 = c2qa.QumodeRegister(3, 2)
@@ -269,5 +281,7 @@ def test_cv_initialize(capsys):
         circuit.cv_initialize([0, 1], qmr6[0])
 
         # saving a state vector for all the registers takes a considerable amount of time
-        state, result, fock_counts = c2qa.util.simulate(circuit, add_save_statevector=False, return_fockcounts=False)
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, add_save_statevector=False, return_fockcounts=False
+        )
         assert result.success
diff --git a/tests/test_discretize.py b/tests/test_discretize.py
index 01b3a19..a4e2045 100644
--- a/tests/test_discretize.py
+++ b/tests/test_discretize.py
@@ -21,7 +21,9 @@ def test_cv_c_d(capsys):
 
         gate = circuit.data[0].operation
         total_steps = 2
-        discretized_params = gate.calculate_segment_params(current_step=1, total_steps=total_steps, keep_state=True)
+        discretized_params = gate.calculate_segment_params(
+            current_step=1, total_steps=total_steps, keep_state=True
+        )
 
         print(f"Original theta={theta}")
         print(f"Discretized params {discretized_params}")
@@ -44,11 +46,15 @@ def test_cv_c_schwinger(capsys):
         phi_1 = random.random()
         theta_2 = random.random()
         phi_2 = random.random()
-        circuit.cv_c_schwinger([beta, theta_1, phi_1, theta_2, phi_2], qmr[0], qmr[1], qr[0])
+        circuit.cv_c_schwinger(
+            [beta, theta_1, phi_1, theta_2, phi_2], qmr[0], qmr[1], qr[0]
+        )
 
         gate = circuit.data[0].operation
         total_steps = 2
-        discretized_params = gate.calculate_segment_params(current_step=1, total_steps=total_steps, keep_state=True)
+        discretized_params = gate.calculate_segment_params(
+            current_step=1, total_steps=total_steps, keep_state=True
+        )
 
         print(f"Original params {(beta, theta_1, phi_1, theta_2, phi_2)}")
         print(f"Discretized params {discretized_params}")
@@ -60,7 +66,9 @@ def test_cv_c_schwinger(capsys):
         assert discretized_params[4] == phi_2
 
 
-@pytest.mark.skip(reason="Enable and test manually with debug breakpoint in__calculate_segment_params to ensure only first param is discretized")
+@pytest.mark.skip(
+    reason="Enable and test manually with debug breakpoint in__calculate_segment_params to ensure only first param is discretized"
+)
 def test_cv_c_schwinger_animate(capsys):
     """The cv_c_schwinger gate should discretize the first param, but the others not"""
     with capsys.disabled():
@@ -75,7 +83,9 @@ def test_cv_c_schwinger_animate(capsys):
         phi_1 = random.random()
         theta_2 = random.random()
         phi_2 = random.random()
-        circuit.cv_c_schwinger([beta, theta_1, phi_1, theta_2, phi_2], qmr[0], qmr[1], qr[0])
+        circuit.cv_c_schwinger(
+            [beta, theta_1, phi_1, theta_2, phi_2], qmr[0], qmr[1], qr[0]
+        )
 
         c2qa.animate.animate_wigner(
             circuit,
@@ -97,8 +107,16 @@ def test_discretize_with_pershot_statevector(capsys):
         circ.cv_delay(duration=100, qumode=qmr[0], unit="ns")
         circ.cv_measure(qmr[0], creg)
 
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=0.02, circuit=circ, time_unit="ns")
-        state, result, fock_counts = c2qa.util.simulate(circ, noise_passes=noise_pass, discretize=True, shots=2, per_shot_state_vector=True)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=0.02, circuit=circ, time_unit="ns"
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            circ,
+            noise_passes=noise_pass,
+            discretize=True,
+            shots=2,
+            per_shot_state_vector=True,
+        )
 
         assert result.success
 
@@ -110,8 +128,8 @@ def simulate_test(discretize: bool):
         cr = qiskit.circuit.ClassicalRegister(1)
         circ = c2qa.CVCircuit(qmr, anc, cr)
 
-        circ.initialize([1, 0], anc[0]) # Ancilla in |g>
-        circ.cv_initialize(3, qmr[0]) # Qumode in |3>
+        circ.initialize([1, 0], anc[0])  # Ancilla in |g>
+        circ.cv_initialize(3, qmr[0])  # Qumode in |3>
 
         # Photon number parity circuit
         circ.h(anc[0])
@@ -120,9 +138,13 @@ def simulate_test(discretize: bool):
         circ.measure(anc[0], cr[0])
 
         # Simulate
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=0.1, circuit=circ, time_unit="µs")
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=0.1, circuit=circ, time_unit="µs"
+        )
 
-        state, result, fock_counts = c2qa.util.simulate(circ, noise_passes=noise_pass, discretize=discretize, shots=3000)
+        state, result, fock_counts = c2qa.util.simulate(
+            circ, noise_passes=noise_pass, discretize=discretize, shots=3000
+        )
         print("##############")
         print(f"Result counts: {result.get_counts()}")
         print(f"Fock counts: {fock_counts}")
@@ -150,9 +172,13 @@ def simulate_test(discretize: bool):
 
         photon_loss_rate = 0.02
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit)
-    
-        state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass, discretize=discretize, shots=200)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit
+        )
+
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, noise_passes=noise_pass, discretize=discretize, shots=200
+        )
         print("##############")
         print(f"Result counts: {result.get_counts()}")
         print(f"Fock counts: {fock_counts}")
@@ -166,6 +192,7 @@ def simulate_test(discretize: bool):
         print("DISCRETIZED")
         simulate_test(discretize=True)
 
+
 def test_manual_vs_auto_discretize(capsys):
     def simulate_test(manually_discretize: bool):
         qmr = c2qa.QumodeRegister(1, 3)
@@ -173,23 +200,30 @@ def simulate_test(manually_discretize: bool):
         cr = qiskit.circuit.ClassicalRegister(1)
         circ = c2qa.CVCircuit(qmr, anc, cr)
 
-        circ.initialize([1, 0], anc[0]) # Ancilla in |g>
-        circ.cv_initialize(3, qmr[0]) # Qumode in |3>
+        circ.initialize([1, 0], anc[0])  # Ancilla in |g>
+        circ.cv_initialize(3, qmr[0])  # Qumode in |3>
 
         # Photon number parity circuit
         circ.h(anc[0])
         if manually_discretize:
-            for _ in range(10): # Manually discretize cv_c_r gate
-                circ.cv_c_r(numpy.pi/20, qmr[0], anc[0], duration=0.1, unit="µs")
+            for _ in range(10):  # Manually discretize cv_c_r gate
+                circ.cv_c_r(numpy.pi / 20, qmr[0], anc[0], duration=0.1, unit="µs")
         else:
-            circ.cv_c_r(numpy.pi/2, qmr[0], anc[0], duration=1, unit="µs")
+            circ.cv_c_r(numpy.pi / 2, qmr[0], anc[0], duration=1, unit="µs")
         circ.h(anc[0])
         circ.measure(anc[0], cr[0])
 
         # Simulate
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=0.1, circuit=circ, time_unit="µs")
-        return c2qa.util.simulate(circ, noise_passes=noise_pass, shots=3000, discretize=(not manually_discretize))
-    
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=0.1, circuit=circ, time_unit="µs"
+        )
+        return c2qa.util.simulate(
+            circ,
+            noise_passes=noise_pass,
+            shots=3000,
+            discretize=(not manually_discretize),
+        )
+
     with capsys.disabled():
         min_percent_diff = 99999
         max_percent_diff = 0
@@ -219,6 +253,6 @@ def simulate_test(manually_discretize: bool):
                 max_percent_diff = max(percent_diff, max_percent_diff)
                 print(f"Key '{key}' percent difference {percent_diff}")
                 assert math.isclose(counts_man[key], counts_auto[key], rel_tol=0.25)
-        
+
         print(f"Min percent diff {min_percent_diff}")
-        print(f"Max percent diff {max_percent_diff}")
\ No newline at end of file
+        print(f"Max percent diff {max_percent_diff}")
diff --git a/tests/test_gates.py b/tests/test_gates.py
index fd94aae..54e4450 100644
--- a/tests/test_gates.py
+++ b/tests/test_gates.py
@@ -89,6 +89,7 @@ def test_conditional_beamsplitter():
     # assert_changed(state, result)
     assert_unchanged(state, result)
 
+
 def test_conditional_schwinger():
     circuit, qmr, qr = create_conditional()
 
@@ -97,7 +98,9 @@ def test_conditional_schwinger():
     phi_1 = random.random()
     theta_2 = random.random()
     phi_2 = random.random()
-    circuit.cv_c_schwinger([beta, theta_1, phi_1, theta_2, phi_2], qmr[0], qmr[1], qr[0])
+    circuit.cv_c_schwinger(
+        [beta, theta_1, phi_1, theta_2, phi_2], qmr[0], qmr[1], qr[0]
+    )
 
     state, result, fock_counts = c2qa.util.simulate(circuit)
 
@@ -154,10 +157,11 @@ def test_displacement_once(capsys):
         state, result, fock_counts = c2qa.util.simulate(circuit)
         assert_changed(state, result)
 
+
 def test_cv_delay():
     circuit, qmr = create_unconditional()
 
-    circuit.cv_delay(100,qmr[0])
+    circuit.cv_delay(100, qmr[0])
 
     state, result, fock_counts = c2qa.util.simulate(circuit)
 
@@ -321,16 +325,25 @@ def test_eswap():
 
     state, result, fock_counts = c2qa.util.simulate(circuit)
 
+
 def test_multiboson_sampling(capsys):
     with capsys.disabled():
-        num_qubits=1
-        num_qumodes=2
-        num_qubits_per_qumode=2
-        qmrA = c2qa.QumodeRegister(num_qumodes = num_qumodes, num_qubits_per_qumode = num_qubits_per_qumode,name="qmrA_initial")
-        qmrB = c2qa.QumodeRegister(num_qumodes = num_qumodes, num_qubits_per_qumode = num_qubits_per_qumode,name="qmrB_initial")
-        qbr = qiskit.QuantumRegister(size=num_qubits,name='qbr_initial')
+        num_qubits = 1
+        num_qumodes = 2
+        num_qubits_per_qumode = 2
+        qmrA = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes,
+            num_qubits_per_qumode=num_qubits_per_qumode,
+            name="qmrA_initial",
+        )
+        qmrB = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes,
+            num_qubits_per_qumode=num_qubits_per_qumode,
+            name="qmrB_initial",
+        )
+        qbr = qiskit.QuantumRegister(size=num_qubits, name="qbr_initial")
         circuit = c2qa.CVCircuit(qmrA, qmrB, qbr)
-        circuit.cv_c_multiboson_sampling([0,1,2,3], qmrA[0], qbr[0])
+        circuit.cv_c_multiboson_sampling([0, 1, 2, 3], qmrA[0], qbr[0])
 
         state, result, fock_counts = c2qa.util.simulate(circuit)
-        assert result.success
\ No newline at end of file
+        assert result.success
diff --git a/tests/test_inconsistent_statevectors.py b/tests/test_inconsistent_statevectors.py
index 46bb22e..8bea992 100644
--- a/tests/test_inconsistent_statevectors.py
+++ b/tests/test_inconsistent_statevectors.py
@@ -32,11 +32,15 @@ def conditional_displacement_gate(circuit, arg_0, arg_1, qbit, qumode):
     op_1 = displacement_operator(arg_1)
 
     circuit.append(
-        qiskit.circuit.library.UnitaryGate(op_0).control(num_ctrl_qubits=1, ctrl_state=0),
+        qiskit.circuit.library.UnitaryGate(op_0).control(
+            num_ctrl_qubits=1, ctrl_state=0
+        ),
         [qbit] + qumode,
     )
     circuit.append(
-        qiskit.circuit.library.UnitaryGate(op_1).control(num_ctrl_qubits=1, ctrl_state=1),
+        qiskit.circuit.library.UnitaryGate(op_1).control(
+            num_ctrl_qubits=1, ctrl_state=1
+        ),
         [qbit] + qumode,
     )
 
diff --git a/tests/test_noise_model.py b/tests/test_noise_model.py
index 6c9c0bf..610cbcd 100644
--- a/tests/test_noise_model.py
+++ b/tests/test_noise_model.py
@@ -40,10 +40,10 @@ def test_noise_model(capsys):
         time = 5.0  # seconds
         kraus_operators = c2qa.kraus.calculate_kraus(
             photon_loss_rates=[photon_loss_rate, photon_loss_rate],
-            time=time, 
+            time=time,
             circuit=circuit,
             op_qubits=[0, 1, 2],
-            qumode_qubit_indices=[0, 1]
+            qumode_qubit_indices=[0, 1],
         )
 
         print("kraus")
@@ -64,10 +64,10 @@ def test_kraus_operators(capsys):
         time = 1.0  # seconds
         kraus_operators = c2qa.kraus.calculate_kraus(
             photon_loss_rates=[photon_loss_rate, photon_loss_rate],
-            time=time, 
+            time=time,
             circuit=circuit,
             op_qubits=[0, 1, 2],
-            qumode_qubit_indices=[0, 1]
+            qumode_qubit_indices=[0, 1],
         )
 
         kraus = qiskit.quantum_info.operators.channel.Kraus(kraus_operators)
@@ -109,7 +109,9 @@ def test_beamsplitter_kraus_operators(capsys):
         num_qumodes = 2
         qubits_per_mode = 2
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         circuit = c2qa.CVCircuit(qmr)
         circuit.cv_initialize(2, qmr[0])
         circuit.cv_bs(1, qmr[1], qmr[0], duration=100, unit="ns")
@@ -117,10 +119,10 @@ def test_beamsplitter_kraus_operators(capsys):
         time = 1.0  # seconds
         kraus_operators = c2qa.kraus.calculate_kraus(
             photon_loss_rates=[photon_loss_rate, photon_loss_rate],
-            time=time, 
+            time=time,
             circuit=circuit,
             op_qubits=[2, 3, 0, 1],
-            qumode_qubit_indices=[0, 1, 2, 3]
+            qumode_qubit_indices=[0, 1, 2, 3],
         )
 
         kraus = qiskit.quantum_info.operators.channel.Kraus(kraus_operators)
@@ -162,15 +164,21 @@ def test_invalid_photon_loss_rate_length(capsys):
         num_qumodes = 2
         qubits_per_mode = 2
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         init_circuit = c2qa.CVCircuit(qmr)
         init_circuit.cv_initialize(2, qmr[0])
         init_circuit.cv_bs(1, qmr[1], qmr[0], duration=100, unit="ns")
-        photon_loss_rates = [1,2,3]  # Should only have two loss rates
+        photon_loss_rates = [1, 2, 3]  # Should only have two loss rates
         time_unit = "ns"
 
         # Should raise Exception for not having proper number of loss rates
-        c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rates, circuit=init_circuit, time_unit=time_unit)
+        c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rates,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
 
 
 def test_valid_photon_loss_rate_length(capsys):
@@ -178,15 +186,21 @@ def test_valid_photon_loss_rate_length(capsys):
         num_qumodes = 2
         qubits_per_mode = 2
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         init_circuit = c2qa.CVCircuit(qmr)
         init_circuit.cv_initialize(2, qmr[0])
         init_circuit.cv_bs(1, qmr[1], qmr[0], duration=100, unit="ns")
-        photon_loss_rates = [1,2]  # Should only have two loss rates
+        photon_loss_rates = [1, 2]  # Should only have two loss rates
         time_unit = "ns"
-        
+
         # Should not raise exception as has proper number of loss rates
-        c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rates, circuit=init_circuit, time_unit=time_unit)
+        c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rates,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
 
 
 def test_noise_with_beamsplitter(capsys):
@@ -194,14 +208,22 @@ def test_noise_with_beamsplitter(capsys):
         num_qumodes = 2
         qubits_per_mode = 2
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         init_circuit = c2qa.CVCircuit(qmr)
         init_circuit.cv_initialize(2, qmr[0])
         init_circuit.cv_bs(1, qmr[1], qmr[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=init_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(init_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            init_circuit, noise_passes=noise_pass
+        )
 
 
 def test_noise_with_beamsplitter_diff_cutoff(capsys):
@@ -214,8 +236,14 @@ def test_noise_with_beamsplitter_diff_cutoff(capsys):
         init_circuit.cv_bs(1, qmr1[0], qmr2[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=init_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(init_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            init_circuit, noise_passes=noise_pass
+        )
 
 
 @pytest.mark.skip(reason="This test takes nearly 30 minutes to pass on Github...")
@@ -230,8 +258,14 @@ def test_noise_with_cbs_diff_cutoff(capsys):
         init_circuit.cv_c_bs(1, qmr1[0], qmr2[0], qbr[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=init_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(init_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            init_circuit, noise_passes=noise_pass
+        )
 
 
 def test_noise_with_sq2_diff_cutoff(capsys):
@@ -244,9 +278,15 @@ def test_noise_with_sq2_diff_cutoff(capsys):
         init_circuit.cv_sq2(1, qmr1[0], qmr2[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=init_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(init_circuit, noise_passes=noise_pass)
-    
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            init_circuit, noise_passes=noise_pass
+        )
+
 
 def test_noise_with_cnd_beamsplitter(capsys):
     with capsys.disabled():
@@ -254,15 +294,23 @@ def test_noise_with_cnd_beamsplitter(capsys):
         qubits_per_mode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         init_circuit = c2qa.CVCircuit(qmr, qbr)
         init_circuit.cv_initialize(2, qmr[0])
         init_circuit.cv_c_bs(1, qmr[1], qmr[0], qbr[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=init_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(init_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            init_circuit, noise_passes=noise_pass
+        )
 
 
 def test_photon_loss_pass_with_conditional(capsys):
@@ -271,15 +319,23 @@ def test_photon_loss_pass_with_conditional(capsys):
         qubits_per_mode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         init_circuit = c2qa.CVCircuit(qmr, qbr)
         init_circuit.cv_initialize(2, qmr[0])
         init_circuit.cv_c_d(1, qmr[0], qbr[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=init_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(init_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=init_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            init_circuit, noise_passes=noise_pass
+        )
 
 
 def test_photon_loss_pass_delay_without_unit(capsys):
@@ -288,17 +344,25 @@ def test_photon_loss_pass_delay_without_unit(capsys):
         qubits_per_mode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
 
         fail_circuit = c2qa.CVCircuit(qmr, qbr)
         fail_circuit.cv_initialize(2, qmr[0])
-        fail_circuit.delay(duration=100) #, unit="ns")
+        fail_circuit.delay(duration=100)  # , unit="ns")
         fail_circuit.cv_c_d(1, qmr[0], qbr[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=fail_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(fail_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=fail_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            fail_circuit, noise_passes=noise_pass
+        )
         assert result.success
 
 
@@ -308,7 +372,9 @@ def test_photon_loss_pass_delay_with_unit(capsys):
         qubits_per_mode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
 
         pass_circuit = c2qa.CVCircuit(qmr, qbr)
@@ -317,8 +383,14 @@ def test_photon_loss_pass_delay_with_unit(capsys):
         pass_circuit.cv_c_d(1, qmr[0], qbr[0], duration=100, unit="ns")
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=pass_circuit, time_unit=time_unit)
-        state, result, fock_counts = c2qa.util.simulate(pass_circuit, noise_passes=noise_pass)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=pass_circuit,
+            time_unit=time_unit,
+        )
+        state, result, fock_counts = c2qa.util.simulate(
+            pass_circuit, noise_passes=noise_pass
+        )
         assert result.success
 
 
@@ -335,7 +407,9 @@ def test_animate_photon_loss_pass(capsys):
 
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit
+        )
 
         wigner_filename = "tests/test_animate_photon_loss_pass.gif"
         c2qa.animate.animate_wigner(
@@ -360,7 +434,9 @@ def test_animate_photon_loss_pass_with_epsilon(capsys):
 
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit
+        )
 
         wigner_filename = "tests/test_animate_photon_loss_pas_with_epsilon.gif"
         c2qa.animate.animate_wigner(
@@ -385,7 +461,9 @@ def test_photon_loss_pass_no_displacement(capsys):
 
         photon_loss_rate = 0.01
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit
+        )
 
         # state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
 
@@ -411,7 +489,9 @@ def test_photon_loss_pass_slow_displacement(capsys):
 
         photon_loss_rate = 0.02
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit
+        )
 
         # state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
 
@@ -431,7 +511,9 @@ def test_photon_loss_pass_slow_conditional_displacement(capsys):
         num_qubits_per_qumode = 4
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         circuit = c2qa.CVCircuit(qmr, qbr)
 
@@ -441,7 +523,9 @@ def test_photon_loss_pass_slow_conditional_displacement(capsys):
 
         photon_loss_rate = 0.02
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit)
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit
+        )
 
         # state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
 
@@ -460,7 +544,9 @@ def test_photon_loss_instruction(capsys):
         num_qubits_per_qumode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         circuit = c2qa.CVCircuit(qmr, qbr)
 
@@ -472,9 +558,16 @@ def test_photon_loss_instruction(capsys):
 
         photon_loss_rate = 0.02
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit, instructions=["cD"])
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=circuit,
+            time_unit=time_unit,
+            instructions=["cD"],
+        )
 
-        state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, noise_passes=noise_pass
+        )
         assert result.success
 
 
@@ -484,7 +577,9 @@ def test_photon_loss_qumode(capsys):
         num_qubits_per_qumode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         circuit = c2qa.CVCircuit(qmr, qbr)
 
@@ -495,9 +590,16 @@ def test_photon_loss_qumode(capsys):
 
         photon_loss_rate = 0.02
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit, qumodes=qmr[1])
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=circuit,
+            time_unit=time_unit,
+            qumodes=qmr[1],
+        )
 
-        state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, noise_passes=noise_pass
+        )
         assert result.success
 
 
@@ -507,7 +609,9 @@ def test_photon_loss_instruction_qumode(capsys):
         num_qubits_per_qumode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         circuit = c2qa.CVCircuit(qmr, qbr)
 
@@ -518,9 +622,17 @@ def test_photon_loss_instruction_qumode(capsys):
 
         photon_loss_rate = 0.02
         time_unit = "ns"
-        noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit=time_unit, instructions=["cD"], qumodes=qmr[0])
+        noise_pass = c2qa.kraus.PhotonLossNoisePass(
+            photon_loss_rates=photon_loss_rate,
+            circuit=circuit,
+            time_unit=time_unit,
+            instructions=["cD"],
+            qumodes=qmr[0],
+        )
 
-        state, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, noise_passes=noise_pass
+        )
         assert result.success
 
 
@@ -535,14 +647,16 @@ def test_photon_loss_and_phase_damping(capsys):
         assert result_b.success
 
         assert not allclose(state_a, state_b)
-    
 
-def _build_photon_loss_and_amp_damping_circuit(amp_damp = 0.3, photon_loss_rate = 0.01):
+
+def _build_photon_loss_and_amp_damping_circuit(amp_damp=0.3, photon_loss_rate=0.01):
     num_qumodes = 1
     qubits_per_mode = 2
     num_qubits = 1
 
-    qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode)
+    qmr = c2qa.QumodeRegister(
+        num_qumodes=num_qumodes, num_qubits_per_qumode=qubits_per_mode
+    )
     qbr = qiskit.QuantumRegister(size=num_qubits)
     circuit = c2qa.CVCircuit(qmr, qbr)
     circuit.cv_initialize(2, qmr[0])
@@ -555,7 +669,9 @@ def _build_photon_loss_and_amp_damping_circuit(amp_damp = 0.3, photon_loss_rate
     noise_model.add_quantum_error(phase_error, ["x"], [circuit.get_qubit_index(qbr[0])])
 
     # Initialize PhotonLossNoisePass
-    noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit="ns")
+    noise_pass = c2qa.kraus.PhotonLossNoisePass(
+        photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit="ns"
+    )
 
     return c2qa.util.simulate(circuit, noise_model=noise_model, noise_passes=noise_pass)
 
@@ -579,7 +695,9 @@ def test_relaxation_noise_pass(capsys):
         num_qubits_per_qumode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         circuit = c2qa.CVCircuit(qmr, qbr)
 
@@ -607,7 +725,9 @@ def test_relaxation_and_photon_loss_noise_passes(capsys):
         num_qubits_per_qumode = 2
         num_qubits = 1
 
-        qmr = c2qa.QumodeRegister(num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode)
+        qmr = c2qa.QumodeRegister(
+            num_qumodes=num_qumodes, num_qubits_per_qumode=num_qubits_per_qumode
+        )
         qbr = qiskit.QuantumRegister(size=num_qubits)
         circuit = c2qa.CVCircuit(qmr, qbr)
 
@@ -622,7 +742,11 @@ def test_relaxation_and_photon_loss_noise_passes(capsys):
         noise_passes.append(RelaxationNoisePass(t1s, t2s))
 
         photon_loss_rate = 0.02
-        noise_passes.append(c2qa.kraus.PhotonLossNoisePass(photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit="ns"))
+        noise_passes.append(
+            c2qa.kraus.PhotonLossNoisePass(
+                photon_loss_rates=photon_loss_rate, circuit=circuit, time_unit="ns"
+            )
+        )
 
         filename = "tests/test_relaxation_and_photon_loss_noise_passes.gif"
         c2qa.animate.animate_wigner(
@@ -643,8 +767,8 @@ def test_multi_qumode_loss_probability(capsys):
 
         circuit.cv_initialize(1, qmr[0])
         circuit.cv_initialize(1, qmr[1])
-        circuit.cv_bs(np.pi/4, qmr[0], qmr[1], duration=100, unit="ns")
-        circuit.cv_bs(-np.pi/4, qmr[0], qmr[1], duration=100, unit="ns")
+        circuit.cv_bs(np.pi / 4, qmr[0], qmr[1], duration=100, unit="ns")
+        circuit.cv_bs(-np.pi / 4, qmr[0], qmr[1], duration=100, unit="ns")
 
         photon_loss_rate = 10000000
         noise_pass = c2qa.kraus.PhotonLossNoisePass(photon_loss_rate, circuit)
@@ -654,9 +778,13 @@ def test_multi_qumode_loss_probability(capsys):
         for i in range(20):
             print("----------------------")
             print(f"Iteration {i}")
-            state_vector, result, fock_counts = c2qa.util.simulate(circuit, noise_passes=noise_pass)
+            state_vector, result, fock_counts = c2qa.util.simulate(
+                circuit, noise_passes=noise_pass
+            )
             # plot_histogram(result.get_counts(circuit), filename=f"tests/test_manual_validate_beamsplitter-{i}.png")
-            occupation, fock_states = c2qa.util.stateread(state_vector, 0, num_qumodes, 2**num_qubits_per_qumode,verbose=True)
+            occupation, fock_states = c2qa.util.stateread(
+                state_vector, 0, num_qumodes, 2**num_qubits_per_qumode, verbose=True
+            )
 
             for qumode_state, qubit_state, amplitude in fock_states:
                 # print(f"{qumode_state} {qubit_state} {amplitude}")
@@ -664,8 +792,11 @@ def test_multi_qumode_loss_probability(capsys):
                 qumode2 = qumode_state[1]
                 probability = amplitude**2
 
-                if (qumode1 == 1 and qumode2 == 0) or (qumode1 == 0 and qumode1 == 1) and math.isclose(probability, 0.5, 0.03):
+                if (
+                    (qumode1 == 1 and qumode2 == 0)
+                    or (qumode1 == 0 and qumode1 == 1)
+                    and math.isclose(probability, 0.5, 0.03)
+                ):
                     fifty_fifty = True
-            
+
         assert fifty_fifty
-        
diff --git a/tests/test_util.py b/tests/test_util.py
index 23e40b4..cd9c684 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -148,31 +148,43 @@ def test_stateread(capsys):
             verbose=True,
         )
 
+
 def test_fockmap(capsys):
     with capsys.disabled():
 
         # Build rand array of rand dim between 1 and 100, use fockmap to populate initally empty array, and assert that final array is equal to rand array
-        for _ in range(10): # Repeat test 10 times
+        for _ in range(10):  # Repeat test 10 times
             dim = numpy.random.randint(low=1, high=101)
             randarray = numpy.random.uniform(low=0, high=1, size=(dim, dim))
 
             testmatrix = numpy.zeros((dim, dim))
             for i in range(dim):
-                for j in range (dim):
+                for j in range(dim):
                     testmatrix = c2qa.util.fockmap(testmatrix, j, i, randarray[i, j])
-                     
-            assert((testmatrix == randarray).all())
+
+            assert (testmatrix == randarray).all()
 
         # Check fockmap using numpy.outer
         matrix = numpy.zeros((4, 4))
-        assert((c2qa.util.fockmap(matrix, 0, 0) == numpy.outer([1, 0, 0 ,0], [1, 0, 0, 0])).all()) # |0><0|
-        assert((c2qa.util.fockmap(matrix, 1, [3, 2], [1, 0.5]) == (numpy.outer([0, 0, 0 ,1], [0, 1, 0, 0]) + 0.5 * numpy.outer([0, 0, 1 ,0], [0, 1, 0, 0]))).all()) # |3><1| + 0.5|2><1|
-        assert((c2qa.util.fockmap(matrix, 1, [3, 2, 1]) == c2qa.util.fockmap(matrix, [1, 1, 1], [3, 2, 1])).all()) # |3><1| + |2><1| + |1><1|
+        assert (
+            c2qa.util.fockmap(matrix, 0, 0) == numpy.outer([1, 0, 0, 0], [1, 0, 0, 0])
+        ).all()  # |0><0|
+        assert (
+            c2qa.util.fockmap(matrix, 1, [3, 2], [1, 0.5])
+            == (
+                numpy.outer([0, 0, 0, 1], [0, 1, 0, 0])
+                + 0.5 * numpy.outer([0, 0, 1, 0], [0, 1, 0, 0])
+            )
+        ).all()  # |3><1| + 0.5|2><1|
+        assert (
+            c2qa.util.fockmap(matrix, 1, [3, 2, 1])
+            == c2qa.util.fockmap(matrix, [1, 1, 1], [3, 2, 1])
+        ).all()  # |3><1| + |2><1| + |1><1|
 
         # Check the types which are accepted for each arg.
         for i in range(10):
             # Nested list, numpy.ndarray
-            m_types = [[[0, 0],[0 ,0]], numpy.zeros((2, 2))] 
+            m_types = [[[0, 0], [0, 0]], numpy.zeros((2, 2))]
 
             # int, list
             fi_types = [0, [1, 0]]
@@ -180,7 +192,7 @@ def test_fockmap(capsys):
             # int, list
             fo_types = [1, [1, 0]]
 
-            #int, float, complex, empty list, list, numpy.ndarray
+            # int, float, complex, empty list, list, numpy.ndarray
             amp_types = [1, 1.0, 1j, [], [1, 1], numpy.array([1, 1])]
 
             # Generate random indices to test for
@@ -192,11 +204,21 @@ def test_fockmap(capsys):
                 amp_index = numpy.random.randint(0, 4)
             else:
                 amp_index = numpy.random.randint(3, 5)
-            
+
             # Assert that output is a numpy.ndarray
-            assert(type(c2qa.util.fockmap(m_types[m_index], fi_types[fi_index], fo_types[fo_index], amp_types[amp_index])) == numpy.ndarray)
+            assert (
+                type(
+                    c2qa.util.fockmap(
+                        m_types[m_index],
+                        fi_types[fi_index],
+                        fo_types[fo_index],
+                        amp_types[amp_index],
+                    )
+                )
+                == numpy.ndarray
+            )
+
 
-        
 def test_circuit_avg_photon_num(capsys):
     with capsys.disabled():
         # Create two qumode registers containing 2 qumodes and 1 qumode respectively.
@@ -205,9 +227,9 @@ def test_circuit_avg_photon_num(capsys):
         circ = c2qa.CVCircuit(qmr1, qmr2)
 
         # Initialize the three qumodes to |3>, |4>, |5> Fock states.
-        circ.cv_initialize(3, qmr1[0]) # Qumode in |3>
-        circ.cv_initialize(4, qmr1[1]) # Qumode in |4>
-        circ.cv_initialize(5, qmr2[0]) # Qumode in |5>
+        circ.cv_initialize(3, qmr1[0])  # Qumode in |3>
+        circ.cv_initialize(4, qmr1[1])  # Qumode in |4>
+        circ.cv_initialize(5, qmr2[0])  # Qumode in |5>
 
         # Print out the indices of qubits in qumodes, grouped by qumode
         print(circ.qumode_qubits_indices_grouped)
@@ -218,27 +240,33 @@ def test_circuit_avg_photon_num(capsys):
 
         avg_photon_num = c2qa.util.avg_photon_num(circ, state)
         print(avg_photon_num)
-        assert([3.0, 4.0, 5.0] == avg_photon_num)
+        assert [3.0, 4.0, 5.0] == avg_photon_num
 
 
 def test_qumode_avg_photon_num(capsys):
     with capsys.disabled():
-        for _ in range(5): # Repeat test 5 times
+        for _ in range(5):  # Repeat test 5 times
             # Decimals
             decimals = numpy.random.randint(1, 6)
-            
+
             # Generate random vector
             dim = numpy.random.randint(1, 11)
-            vector = numpy.random.uniform(-1, 1, dim) + 1.j * numpy.random.uniform(-1, 1, dim)
-            
+            vector = numpy.random.uniform(-1, 1, dim) + 1.0j * numpy.random.uniform(
+                -1, 1, dim
+            )
+
             # Compute magnitude of each element within vector, and norm of vector
             element_norm = numpy.multiply(vector, numpy.conjugate(vector))
             norm = numpy.sum(element_norm)
 
             # Dot product between number operator and magnitude
-            avg_num = numpy.mean(numpy.dot(element_norm, numpy.array(range(dim))))/norm
-            
+            avg_num = (
+                numpy.mean(numpy.dot(element_norm, numpy.array(range(dim)))) / norm
+            )
+
             # Average photon number of statevector, density matrix, and random vector must all match
-            assert(c2qa.util.qumode_avg_photon_num(Statevector(vector), decimals) == c2qa.util.qumode_avg_photon_num(DensityMatrix(vector), decimals) == round(avg_num.real, decimals))
-            
-            
\ No newline at end of file
+            assert (
+                c2qa.util.qumode_avg_photon_num(Statevector(vector), decimals)
+                == c2qa.util.qumode_avg_photon_num(DensityMatrix(vector), decimals)
+                == round(avg_num.real, decimals)
+            )
diff --git a/tests/test_wigner.py b/tests/test_wigner.py
index 9b39d6b..67305c3 100644
--- a/tests/test_wigner.py
+++ b/tests/test_wigner.py
@@ -58,7 +58,10 @@ def test_plot_wigner_projection(capsys):
         circuit.cv_c_d(dist, qmr[0], qr[0])
         # circuit.cv_d(dist, qmr[0])
 
-        c2qa.wigner.plot_wigner_projection(circuit, qr[0], file="tests/interference.png")
+        c2qa.wigner.plot_wigner_projection(
+            circuit, qr[0], file="tests/interference.png"
+        )
+
 
 def test_cat_state_wigner_plot(capsys):
     with capsys.disabled():
@@ -81,7 +84,9 @@ def test_cat_state_wigner_plot(capsys):
         circuit.measure(qr[0], cr[0])
 
         # conditional_state_vector=True will return two state vectors, one for 0 and 1 classical register value
-        state, result, fock_counts = c2qa.util.simulate(circuit, conditional_state_vector=True)
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, conditional_state_vector=True
+        )
         even_state = state["0x0"]
         odd_state = state["0x1"]
 
@@ -138,7 +143,9 @@ def test_wigner_mle(capsys):
         circuit.cv_c_d(dist, qmr[0], qr[0])
         circuit.h(qr[0])
 
-        state, result, fock_counts = c2qa.util.simulate(circuit, per_shot_state_vector=True)
+        state, result, fock_counts = c2qa.util.simulate(
+            circuit, per_shot_state_vector=True
+        )
         wigner = c2qa.wigner.wigner_mle(state)
         assert wigner is not None
         print(wigner)
@@ -182,4 +189,4 @@ def test_plot_zero_contour(capsys):
         data = numpy.zeros((200, 200)).tolist()
         filename = "tests/test_plot_zero_contour.png"
         c2qa.wigner.plot(data, file=filename)
-        assert Path(filename).is_file()
\ No newline at end of file
+        assert Path(filename).is_file()