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Qiskit__qiskit-4465 | You will be provided with a partial code base and an issue statement explaining a problem to resolve.
<issue>
`initialize` and `Statevector` don't play nicely
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Aer version**: 0.5.1
- **Python version**: 3.7.3
- **Operating system**: OSX
### What is the current behavior?
Using `initialize` in a circuit and then running with `Statevector` results in the error "Cannot apply Instruction: reset"
### Steps to reproduce the problem
```
import qiskit as qk
import qiskit.quantum_info as qi
from numpy import sqrt
n = 2
ket0 = [1/sqrt(2),0,0,1/sqrt(2)]
qc = qk.QuantumCircuit(n)
qc.initialize(ket0,range(n))
ket_qi = qi.Statevector.from_instruction(qc)
```
</issue>
<code>
[start of README.md]
1 # Qiskit Terra
2
3 [](https://opensource.org/licenses/Apache-2.0)[](https://travis-ci.com/Qiskit/qiskit-terra)[](https://github.com/Qiskit/qiskit-terra/releases)[](https://pypi.org/project/qiskit-terra/)[](https://coveralls.io/github/Qiskit/qiskit-terra?branch=master)
4
5 **Qiskit** is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms.
6
7 Qiskit is made up of elements that work together to enable quantum computing. This element is **Terra** and is the foundation on which the rest of Qiskit is built.
8
9 ## Installation
10
11 We encourage installing Qiskit via the pip tool (a python package manager), which installs all Qiskit elements, including Terra.
12
13 ```bash
14 pip install qiskit
15 ```
16
17 PIP will handle all dependencies automatically and you will always install the latest (and well-tested) version.
18
19 To install from source, follow the instructions in the [documentation](https://qiskit.org/documentation/contributing_to_qiskit.html#install-terra-from-source).
20
21 ## Creating Your First Quantum Program in Qiskit Terra
22
23 Now that Qiskit is installed, it's time to begin working with Terra.
24
25 We are ready to try out a quantum circuit example, which is simulated locally using
26 the Qiskit BasicAer element. This is a simple example that makes an entangled state.
27
28 ```
29 $ python
30 ```
31
32 ```python
33 >>> from qiskit import *
34 >>> qc = QuantumCircuit(2, 2)
35 >>> qc.h(0)
36 >>> qc.cx(0, 1)
37 >>> qc.measure([0,1], [0,1])
38 >>> backend_sim = BasicAer.get_backend('qasm_simulator')
39 >>> result = backend_sim.run(assemble(qc)).result()
40 >>> print(result.get_counts(qc))
41 ```
42
43 In this case, the output will be:
44
45 ```python
46 {'00': 513, '11': 511}
47 ```
48
49 A script is available [here](examples/python/ibmq/hello_quantum.py), where we also show how to
50 run the same program on a real quantum computer via IBMQ.
51
52 ### Executing your code on a real quantum chip
53
54 You can also use Qiskit to execute your code on a
55 **real quantum chip**.
56 In order to do so, you need to configure Qiskit for using the credentials in
57 your IBM Q account:
58
59 #### Configure your IBMQ credentials
60
61 1. Create an _[IBM Q](https://quantum-computing.ibm.com) > Account_ if you haven't already done so.
62
63 2. Get an API token from the IBM Q website under _My Account > API Token_ and the URL for the account.
64
65 3. Take your token and url from step 2, here called `MY_API_TOKEN`, `MY_URL`, and run:
66
67 ```python
68 >>> from qiskit import IBMQ
69 >>> IBMQ.save_account('MY_API_TOKEN', 'MY_URL')
70 ```
71
72 After calling `IBMQ.save_account()`, your credentials will be stored on disk.
73 Once they are stored, at any point in the future you can load and use them
74 in your program simply via:
75
76 ```python
77 >>> from qiskit import IBMQ
78 >>> IBMQ.load_account()
79 ```
80
81 Those who do not want to save their credentials to disk should use instead:
82
83 ```python
84 >>> from qiskit import IBMQ
85 >>> IBMQ.enable_account('MY_API_TOKEN')
86 ```
87
88 and the token will only be active for the session. For examples using Terra with real
89 devices we have provided a set of examples in **examples/python** and we suggest starting with [using_qiskit_terra_level_0.py](examples/python/using_qiskit_terra_level_0.py) and working up in
90 the levels.
91
92 ## Contribution Guidelines
93
94 If you'd like to contribute to Qiskit Terra, please take a look at our
95 [contribution guidelines](CONTRIBUTING.md). This project adheres to Qiskit's [code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.
96
97 We use [GitHub issues](https://github.com/Qiskit/qiskit-terra/issues) for tracking requests and bugs. Please
98 [join the Qiskit Slack community](https://join.slack.com/t/qiskit/shared_invite/zt-e4sscbg2-p8NHTezPVkC3r8nV6BIUVw)
99 and use our [Qiskit Slack channel](https://qiskit.slack.com) for discussion and simple questions.
100 For questions that are more suited for a forum we use the Qiskit tag in the [Stack Exchange](https://quantumcomputing.stackexchange.com/questions/tagged/qiskit).
101
102 ## Next Steps
103
104 Now you're set up and ready to check out some of the other examples from our
105 [Qiskit Tutorials](https://github.com/Qiskit/qiskit-tutorials) repository.
106
107 ## Authors and Citation
108
109 Qiskit Terra is the work of [many people](https://github.com/Qiskit/qiskit-terra/graphs/contributors) who contribute
110 to the project at different levels. If you use Qiskit, please cite as per the included [BibTeX file](https://github.com/Qiskit/qiskit/blob/master/Qiskit.bib).
111
112 ## Changelog and Release Notes
113
114 The changelog for a particular release is dynamically generated and gets
115 written to the release page on Github for each release. For example, you can
116 find the page for the `0.9.0` release here:
117
118 https://github.com/Qiskit/qiskit-terra/releases/tag/0.9.0
119
120 The changelog for the current release can be found in the releases tab:
121 
122 The changelog provides a quick overview of noteable changes for a given
123 release.
124
125 Additionally, as part of each release detailed release notes are written to
126 document in detail what has changed as part of a release. This includes any
127 documentation on potential breaking changes on upgrade and new features.
128 For example, You can find the release notes for the `0.9.0` release in the
129 Qiskit documentation here:
130
131 https://qiskit.org/documentation/release_notes.html#terra-0-9
132
133 ## License
134
135 [Apache License 2.0](LICENSE.txt)
136
[end of README.md]
[start of qiskit/circuit/__init__.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 """
16 ========================================
17 Quantum Circuits (:mod:`qiskit.circuit`)
18 ========================================
19
20 .. currentmodule:: qiskit.circuit
21
22 Overview
23 ========
24
25 The fundamental element of quantum computing is the **quantum circuit**.
26 A quantum circuit is a computational routine consisting of coherent quantum
27 operations on quantum data, such as qubits. It is an ordered sequence of quantum
28 gates, measurements and resets, which may be conditioned on real-time classical
29 computation. A set of quantum gates is said to be universal if any unitary
30 transformation of the quantum data can be efficiently approximated arbitrarily well
31 as as sequence of gates in the set. Any quantum program can be represented by a
32 sequence of quantum circuits and classical near-time computation.
33
34 In Qiskit, this core element is represented by the :class:`QuantumCircuit` class.
35 Below is an example of a quantum circuit that makes a three-qubit GHZ state
36 defined as:
37
38 .. math::
39
40 |\\psi\\rangle = \\left(|000\\rangle+|111\\rangle\\right)/\\sqrt{2}
41
42
43 .. jupyter-execute::
44
45 from qiskit import QuantumCircuit
46 # Create a circuit with a register of three qubits
47 circ = QuantumCircuit(3)
48 # H gate on qubit 0, putting this qubit in a superposition of |0> + |1>.
49 circ.h(0)
50 # A CX (CNOT) gate on control qubit 0 and target qubit 1 generating a Bell state.
51 circ.cx(0, 1)
52 # CX (CNOT) gate on control qubit 0 and target qubit 2 resulting in a GHZ state.
53 circ.cx(0, 2)
54 # Draw the circuit
55 circ.draw()
56
57
58 Supplementary Information
59 =========================
60
61 .. container:: toggle
62
63 .. container:: header
64
65 **Quantum Circuit Properties**
66
67 When constructing quantum circuits, there are several properties that help quantify
68 the "size" of the circuits, and their ability to be run on a noisy quantum device.
69 Some of these, like number of qubits, are straightforward to understand, while others
70 like depth and number of tensor components require a bit more explanation. Here we will
71 explain all of these properties, and, in preparation for understanding how circuits change
72 when run on actual devices, highlight the conditions under which they change.
73
74 Consider the following circuit:
75
76 .. jupyter-execute::
77
78 from qiskit import QuantumCircuit
79 qc = QuantumCircuit(12)
80 for idx in range(5):
81 qc.h(idx)
82 qc.cx(idx, idx+5)
83
84 qc.cx(1, 7)
85 qc.x(8)
86 qc.cx(1, 9)
87 qc.x(7)
88 qc.cx(1, 11)
89 qc.swap(6, 11)
90 qc.swap(6, 9)
91 qc.swap(6, 10)
92 qc.x(6)
93 qc.draw()
94
95 From the plot, it is easy to see that this circuit has 12 qubits, and a collection of
96 Hadamard, CNOT, X, and SWAP gates. But how to quantify this programmatically? Because we
97 can do single-qubit gates on all the qubits simultaneously, the number of qubits in this
98 circuit is equal to the **width** of the circuit:
99
100 .. jupyter-execute::
101
102 qc.width()
103
104
105 We can also just get the number of qubits directly:
106
107 .. jupyter-execute::
108
109 qc.num_qubits
110
111
112 .. important::
113
114 For a quantum circuit composed from just qubits, the circuit width is equal
115 to the number of qubits. This is the definition used in quantum computing. However,
116 for more complicated circuits with classical registers, and classically controlled gates,
117 this equivalence breaks down. As such, from now on we will not refer to the number of
118 qubits in a quantum circuit as the width.
119
120
121 It is also straightforward to get the number and type of the gates in a circuit using
122 :meth:`QuantumCircuit.count_ops`:
123
124 .. jupyter-execute::
125
126 qc.count_ops()
127
128
129 We can also get just the raw count of operations by computing the circuits
130 :meth:`QuantumCircuit.size`:
131
132 .. jupyter-execute::
133
134 qc.size()
135
136
137 A particularly important circuit property is known as the circuit **depth**. The depth
138 of a quantum circuit is a measure of how many "layers" of quantum gates, executed in
139 parallel, it takes to complete the computation defined by the circuit. Because quantum
140 gates take time to implement, the depth of a circuit roughly corresponds to the amount of
141 time it takes the quantum computer to execute the circuit. Thus, the depth of a circuit
142 is one important quantity used to measure if a quantum circuit can be run on a device.
143
144 The depth of a quantum circuit has a mathematical definition as the longest path in a
145 directed acyclic graph (DAG). However, such a definition is a bit hard to grasp, even for
146 experts. Fortunately, the depth of a circuit can be easily understood by anyone familiar
147 with playing `Tetris <https://en.wikipedia.org/wiki/Tetris>`_. Lets see how to compute this
148 graphically:
149
150 .. image:: /source_images/depth.gif
151
152
153 .. raw:: html
154
155 <br><br>
156
157
158 We can verify our graphical result using :meth:`QuantumCircuit.depth`:
159
160 .. jupyter-execute::
161
162 qc.depth()
163
164
165 .. raw:: html
166
167 <br>
168
169 Quantum Circuit API
170 ===================
171
172 Quantum Circuit Construction
173 ----------------------------
174
175 .. autosummary::
176 :toctree: ../stubs/
177
178 QuantumCircuit
179 QuantumRegister
180 Qubit
181 ClassicalRegister
182 Clbit
183
184 Gates and Instructions
185 ----------------------
186
187 .. autosummary::
188 :toctree: ../stubs/
189
190 Gate
191 ControlledGate
192 Measure
193 Reset
194 Instruction
195 InstructionSet
196 EquivalenceLibrary
197
198 Parametric Quantum Circuits
199 ---------------------------
200
201 .. autosummary::
202 :toctree: ../stubs/
203
204 Parameter
205 ParameterVector
206 ParameterExpression
207
208 Random Circuits
209 ---------------
210
211 .. autosummary::
212 :toctree: ../stubs/
213
214 random.random_circuit
215 """
216 from .quantumcircuit import QuantumCircuit
217 from .classicalregister import ClassicalRegister, Clbit
218 from .quantumregister import QuantumRegister, Qubit
219 from .gate import Gate
220 # pylint: disable=cyclic-import
221 from .controlledgate import ControlledGate
222 from .instruction import Instruction
223 from .instructionset import InstructionSet
224 from .barrier import Barrier
225 from .measure import Measure
226 from .reset import Reset
227 from .parameter import Parameter
228 from .parametervector import ParameterVector
229 from .parameterexpression import ParameterExpression
230 from .equivalence import EquivalenceLibrary
231
[end of qiskit/circuit/__init__.py]
[start of qiskit/providers/basicaer/qasm_simulator.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 # pylint: disable=arguments-differ
16
17 """Contains a (slow) Python simulator.
18
19 It simulates a qasm quantum circuit (an experiment) that has been compiled
20 to run on the simulator. It is exponential in the number of qubits.
21
22 The simulator is run using
23
24 .. code-block:: python
25
26 QasmSimulatorPy().run(qobj)
27
28 Where the input is a Qobj object and the output is a BasicAerJob object, which can
29 later be queried for the Result object. The result will contain a 'memory' data
30 field, which is a result of measurements for each shot.
31 """
32
33 import uuid
34 import time
35 import logging
36
37 from math import log2
38 from collections import Counter
39 import numpy as np
40
41 from qiskit.util import local_hardware_info
42 from qiskit.providers.models import QasmBackendConfiguration
43 from qiskit.result import Result
44 from qiskit.providers import BaseBackend
45 from qiskit.providers.basicaer.basicaerjob import BasicAerJob
46 from .exceptions import BasicAerError
47 from .basicaertools import single_gate_matrix
48 from .basicaertools import cx_gate_matrix
49 from .basicaertools import einsum_vecmul_index
50
51 logger = logging.getLogger(__name__)
52
53
54 class QasmSimulatorPy(BaseBackend):
55 """Python implementation of a qasm simulator."""
56
57 MAX_QUBITS_MEMORY = int(log2(local_hardware_info()['memory'] * (1024 ** 3) / 16))
58
59 DEFAULT_CONFIGURATION = {
60 'backend_name': 'qasm_simulator',
61 'backend_version': '2.0.0',
62 'n_qubits': min(24, MAX_QUBITS_MEMORY),
63 'url': 'https://github.com/Qiskit/qiskit-terra',
64 'simulator': True,
65 'local': True,
66 'conditional': True,
67 'open_pulse': False,
68 'memory': True,
69 'max_shots': 65536,
70 'coupling_map': None,
71 'description': 'A python simulator for qasm experiments',
72 'basis_gates': ['u1', 'u2', 'u3', 'cx', 'id', 'unitary'],
73 'gates': [
74 {
75 'name': 'u1',
76 'parameters': ['lambda'],
77 'qasm_def': 'gate u1(lambda) q { U(0,0,lambda) q; }'
78 },
79 {
80 'name': 'u2',
81 'parameters': ['phi', 'lambda'],
82 'qasm_def': 'gate u2(phi,lambda) q { U(pi/2,phi,lambda) q; }'
83 },
84 {
85 'name': 'u3',
86 'parameters': ['theta', 'phi', 'lambda'],
87 'qasm_def': 'gate u3(theta,phi,lambda) q { U(theta,phi,lambda) q; }'
88 },
89 {
90 'name': 'cx',
91 'parameters': ['c', 't'],
92 'qasm_def': 'gate cx c,t { CX c,t; }'
93 },
94 {
95 'name': 'id',
96 'parameters': ['a'],
97 'qasm_def': 'gate id a { U(0,0,0) a; }'
98 },
99 {
100 'name': 'unitary',
101 'parameters': ['matrix'],
102 'qasm_def': 'unitary(matrix) q1, q2,...'
103 }
104 ]
105 }
106
107 DEFAULT_OPTIONS = {
108 "initial_statevector": None,
109 "chop_threshold": 1e-15
110 }
111
112 # Class level variable to return the final state at the end of simulation
113 # This should be set to True for the statevector simulator
114 SHOW_FINAL_STATE = False
115
116 def __init__(self, configuration=None, provider=None):
117 super().__init__(configuration=(
118 configuration or QasmBackendConfiguration.from_dict(self.DEFAULT_CONFIGURATION)),
119 provider=provider)
120
121 # Define attributes in __init__.
122 self._local_random = np.random.RandomState()
123 self._classical_memory = 0
124 self._classical_register = 0
125 self._statevector = 0
126 self._number_of_cmembits = 0
127 self._number_of_qubits = 0
128 self._shots = 0
129 self._memory = False
130 self._initial_statevector = self.DEFAULT_OPTIONS["initial_statevector"]
131 self._chop_threshold = self.DEFAULT_OPTIONS["chop_threshold"]
132 self._qobj_config = None
133 # TEMP
134 self._sample_measure = False
135
136 def _add_unitary(self, gate, qubits):
137 """Apply an N-qubit unitary matrix.
138
139 Args:
140 gate (matrix_like): an N-qubit unitary matrix
141 qubits (list): the list of N-qubits.
142 """
143 # Get the number of qubits
144 num_qubits = len(qubits)
145 # Compute einsum index string for 1-qubit matrix multiplication
146 indexes = einsum_vecmul_index(qubits, self._number_of_qubits)
147 # Convert to complex rank-2N tensor
148 gate_tensor = np.reshape(np.array(gate, dtype=complex),
149 num_qubits * [2, 2])
150 # Apply matrix multiplication
151 self._statevector = np.einsum(indexes, gate_tensor, self._statevector,
152 dtype=complex, casting='no')
153
154 def _get_measure_outcome(self, qubit):
155 """Simulate the outcome of measurement of a qubit.
156
157 Args:
158 qubit (int): the qubit to measure
159
160 Return:
161 tuple: pair (outcome, probability) where outcome is '0' or '1' and
162 probability is the probability of the returned outcome.
163 """
164 # Axis for numpy.sum to compute probabilities
165 axis = list(range(self._number_of_qubits))
166 axis.remove(self._number_of_qubits - 1 - qubit)
167 probabilities = np.sum(np.abs(self._statevector) ** 2, axis=tuple(axis))
168 # Compute einsum index string for 1-qubit matrix multiplication
169 random_number = self._local_random.rand()
170 if random_number < probabilities[0]:
171 return '0', probabilities[0]
172 # Else outcome was '1'
173 return '1', probabilities[1]
174
175 def _add_sample_measure(self, measure_params, num_samples):
176 """Generate memory samples from current statevector.
177
178 Args:
179 measure_params (list): List of (qubit, cmembit) values for
180 measure instructions to sample.
181 num_samples (int): The number of memory samples to generate.
182
183 Returns:
184 list: A list of memory values in hex format.
185 """
186 # Get unique qubits that are actually measured and sort in
187 # ascending order
188 measured_qubits = sorted(list({qubit for qubit, cmembit in measure_params}))
189 num_measured = len(measured_qubits)
190 # We use the axis kwarg for numpy.sum to compute probabilities
191 # this sums over all non-measured qubits to return a vector
192 # of measure probabilities for the measured qubits
193 axis = list(range(self._number_of_qubits))
194 for qubit in reversed(measured_qubits):
195 # Remove from largest qubit to smallest so list position is correct
196 # with respect to position from end of the list
197 axis.remove(self._number_of_qubits - 1 - qubit)
198 probabilities = np.reshape(np.sum(np.abs(self._statevector) ** 2,
199 axis=tuple(axis)),
200 2 ** num_measured)
201 # Generate samples on measured qubits as ints with qubit
202 # position in the bit-string for each int given by the qubit
203 # position in the sorted measured_qubits list
204 samples = self._local_random.choice(range(2 ** num_measured),
205 num_samples, p=probabilities)
206 # Convert the ints to bitstrings
207 memory = []
208 for sample in samples:
209 classical_memory = self._classical_memory
210 for qubit, cmembit in measure_params:
211 pos = measured_qubits.index(qubit)
212 qubit_outcome = int((sample & (1 << pos)) >> pos)
213 membit = 1 << cmembit
214 classical_memory = (classical_memory & (~membit)) | (qubit_outcome << cmembit)
215 value = bin(classical_memory)[2:]
216 memory.append(hex(int(value, 2)))
217 return memory
218
219 def _add_qasm_measure(self, qubit, cmembit, cregbit=None):
220 """Apply a measure instruction to a qubit.
221
222 Args:
223 qubit (int): qubit is the qubit measured.
224 cmembit (int): is the classical memory bit to store outcome in.
225 cregbit (int, optional): is the classical register bit to store outcome in.
226 """
227 # get measure outcome
228 outcome, probability = self._get_measure_outcome(qubit)
229 # update classical state
230 membit = 1 << cmembit
231 self._classical_memory = (self._classical_memory & (~membit)) | (int(outcome) << cmembit)
232
233 if cregbit is not None:
234 regbit = 1 << cregbit
235 self._classical_register = \
236 (self._classical_register & (~regbit)) | (int(outcome) << cregbit)
237
238 # update quantum state
239 if outcome == '0':
240 update_diag = [[1 / np.sqrt(probability), 0], [0, 0]]
241 else:
242 update_diag = [[0, 0], [0, 1 / np.sqrt(probability)]]
243 # update classical state
244 self._add_unitary(update_diag, [qubit])
245
246 def _add_qasm_reset(self, qubit):
247 """Apply a reset instruction to a qubit.
248
249 Args:
250 qubit (int): the qubit being rest
251
252 This is done by doing a simulating a measurement
253 outcome and projecting onto the outcome state while
254 renormalizing.
255 """
256 # get measure outcome
257 outcome, probability = self._get_measure_outcome(qubit)
258 # update quantum state
259 if outcome == '0':
260 update = [[1 / np.sqrt(probability), 0], [0, 0]]
261 self._add_unitary(update, [qubit])
262 else:
263 update = [[0, 1 / np.sqrt(probability)], [0, 0]]
264 self._add_unitary(update, [qubit])
265
266 def _validate_initial_statevector(self):
267 """Validate an initial statevector"""
268 # If initial statevector isn't set we don't need to validate
269 if self._initial_statevector is None:
270 return
271 # Check statevector is correct length for number of qubits
272 length = len(self._initial_statevector)
273 required_dim = 2 ** self._number_of_qubits
274 if length != required_dim:
275 raise BasicAerError('initial statevector is incorrect length: ' +
276 '{} != {}'.format(length, required_dim))
277
278 def _set_options(self, qobj_config=None, backend_options=None):
279 """Set the backend options for all experiments in a qobj"""
280 # Reset default options
281 self._initial_statevector = self.DEFAULT_OPTIONS["initial_statevector"]
282 self._chop_threshold = self.DEFAULT_OPTIONS["chop_threshold"]
283 if backend_options is None:
284 backend_options = {}
285
286 # Check for custom initial statevector in backend_options first,
287 # then config second
288 if 'initial_statevector' in backend_options:
289 self._initial_statevector = np.array(backend_options['initial_statevector'],
290 dtype=complex)
291 elif hasattr(qobj_config, 'initial_statevector'):
292 self._initial_statevector = np.array(qobj_config.initial_statevector,
293 dtype=complex)
294 if self._initial_statevector is not None:
295 # Check the initial statevector is normalized
296 norm = np.linalg.norm(self._initial_statevector)
297 if round(norm, 12) != 1:
298 raise BasicAerError('initial statevector is not normalized: ' +
299 'norm {} != 1'.format(norm))
300 # Check for custom chop threshold
301 # Replace with custom options
302 if 'chop_threshold' in backend_options:
303 self._chop_threshold = backend_options['chop_threshold']
304 elif hasattr(qobj_config, 'chop_threshold'):
305 self._chop_threshold = qobj_config.chop_threshold
306
307 def _initialize_statevector(self):
308 """Set the initial statevector for simulation"""
309 if self._initial_statevector is None:
310 # Set to default state of all qubits in |0>
311 self._statevector = np.zeros(2 ** self._number_of_qubits,
312 dtype=complex)
313 self._statevector[0] = 1
314 else:
315 self._statevector = self._initial_statevector.copy()
316 # Reshape to rank-N tensor
317 self._statevector = np.reshape(self._statevector,
318 self._number_of_qubits * [2])
319
320 def _get_statevector(self):
321 """Return the current statevector"""
322 vec = np.reshape(self._statevector, 2 ** self._number_of_qubits)
323 vec[abs(vec) < self._chop_threshold] = 0.0
324 return vec
325
326 def _validate_measure_sampling(self, experiment):
327 """Determine if measure sampling is allowed for an experiment
328
329 Args:
330 experiment (QobjExperiment): a qobj experiment.
331 """
332 # If shots=1 we should disable measure sampling.
333 # This is also required for statevector simulator to return the
334 # correct final statevector without silently dropping final measurements.
335 if self._shots <= 1:
336 self._sample_measure = False
337 return
338
339 # Check for config flag
340 if hasattr(experiment.config, 'allows_measure_sampling'):
341 self._sample_measure = experiment.config.allows_measure_sampling
342 # If flag isn't found do a simple test to see if a circuit contains
343 # no reset instructions, and no gates instructions after
344 # the first measure.
345 else:
346 measure_flag = False
347 for instruction in experiment.instructions:
348 # If circuit contains reset operations we cannot sample
349 if instruction.name == "reset":
350 self._sample_measure = False
351 return
352 # If circuit contains a measure option then we can
353 # sample only if all following operations are measures
354 if measure_flag:
355 # If we find a non-measure instruction
356 # we cannot do measure sampling
357 if instruction.name not in ["measure", "barrier", "id", "u0"]:
358 self._sample_measure = False
359 return
360 elif instruction.name == "measure":
361 measure_flag = True
362 # If we made it to the end of the circuit without returning
363 # measure sampling is allowed
364 self._sample_measure = True
365
366 def run(self, qobj, backend_options=None):
367 """Run qobj asynchronously.
368
369 Args:
370 qobj (Qobj): payload of the experiment
371 backend_options (dict): backend options
372
373 Returns:
374 BasicAerJob: derived from BaseJob
375
376 Additional Information:
377 backend_options: Is a dict of options for the backend. It may contain
378 * "initial_statevector": vector_like
379
380 The "initial_statevector" option specifies a custom initial
381 initial statevector for the simulator to be used instead of the all
382 zero state. This size of this vector must be correct for the number
383 of qubits in all experiments in the qobj.
384
385 Example::
386
387 backend_options = {
388 "initial_statevector": np.array([1, 0, 0, 1j]) / np.sqrt(2),
389 }
390 """
391 self._set_options(qobj_config=qobj.config,
392 backend_options=backend_options)
393 job_id = str(uuid.uuid4())
394 job = BasicAerJob(self, job_id, self._run_job, qobj)
395 job.submit()
396 return job
397
398 def _run_job(self, job_id, qobj):
399 """Run experiments in qobj
400
401 Args:
402 job_id (str): unique id for the job.
403 qobj (Qobj): job description
404
405 Returns:
406 Result: Result object
407 """
408 self._validate(qobj)
409 result_list = []
410 self._shots = qobj.config.shots
411 self._memory = getattr(qobj.config, 'memory', False)
412 self._qobj_config = qobj.config
413 start = time.time()
414 for experiment in qobj.experiments:
415 result_list.append(self.run_experiment(experiment))
416 end = time.time()
417 result = {'backend_name': self.name(),
418 'backend_version': self._configuration.backend_version,
419 'qobj_id': qobj.qobj_id,
420 'job_id': job_id,
421 'results': result_list,
422 'status': 'COMPLETED',
423 'success': True,
424 'time_taken': (end - start),
425 'header': qobj.header.to_dict()}
426
427 return Result.from_dict(result)
428
429 def run_experiment(self, experiment):
430 """Run an experiment (circuit) and return a single experiment result.
431
432 Args:
433 experiment (QobjExperiment): experiment from qobj experiments list
434
435 Returns:
436 dict: A result dictionary which looks something like::
437
438 {
439 "name": name of this experiment (obtained from qobj.experiment header)
440 "seed": random seed used for simulation
441 "shots": number of shots used in the simulation
442 "data":
443 {
444 "counts": {'0x9: 5, ...},
445 "memory": ['0x9', '0xF', '0x1D', ..., '0x9']
446 },
447 "status": status string for the simulation
448 "success": boolean
449 "time_taken": simulation time of this single experiment
450 }
451 Raises:
452 BasicAerError: if an error occurred.
453 """
454 start = time.time()
455 self._number_of_qubits = experiment.config.n_qubits
456 self._number_of_cmembits = experiment.config.memory_slots
457 self._statevector = 0
458 self._classical_memory = 0
459 self._classical_register = 0
460 self._sample_measure = False
461 # Validate the dimension of initial statevector if set
462 self._validate_initial_statevector()
463 # Get the seed looking in circuit, qobj, and then random.
464 if hasattr(experiment.config, 'seed_simulator'):
465 seed_simulator = experiment.config.seed_simulator
466 elif hasattr(self._qobj_config, 'seed_simulator'):
467 seed_simulator = self._qobj_config.seed_simulator
468 else:
469 # For compatibility on Windows force dyte to be int32
470 # and set the maximum value to be (2 ** 31) - 1
471 seed_simulator = np.random.randint(2147483647, dtype='int32')
472
473 self._local_random.seed(seed=seed_simulator)
474 # Check if measure sampling is supported for current circuit
475 self._validate_measure_sampling(experiment)
476
477 # List of final counts for all shots
478 memory = []
479 # Check if we can sample measurements, if so we only perform 1 shot
480 # and sample all outcomes from the final state vector
481 if self._sample_measure:
482 shots = 1
483 # Store (qubit, cmembit) pairs for all measure ops in circuit to
484 # be sampled
485 measure_sample_ops = []
486 else:
487 shots = self._shots
488 for _ in range(shots):
489 self._initialize_statevector()
490 # Initialize classical memory to all 0
491 self._classical_memory = 0
492 self._classical_register = 0
493 for operation in experiment.instructions:
494 conditional = getattr(operation, 'conditional', None)
495 if isinstance(conditional, int):
496 conditional_bit_set = (self._classical_register >> conditional) & 1
497 if not conditional_bit_set:
498 continue
499 elif conditional is not None:
500 mask = int(operation.conditional.mask, 16)
501 if mask > 0:
502 value = self._classical_memory & mask
503 while (mask & 0x1) == 0:
504 mask >>= 1
505 value >>= 1
506 if value != int(operation.conditional.val, 16):
507 continue
508
509 # Check if single gate
510 if operation.name == 'unitary':
511 qubits = operation.qubits
512 gate = operation.params[0]
513 self._add_unitary(gate, qubits)
514 elif operation.name in ('U', 'u1', 'u2', 'u3'):
515 params = getattr(operation, 'params', None)
516 qubit = operation.qubits[0]
517 gate = single_gate_matrix(operation.name, params)
518 self._add_unitary(gate, [qubit])
519 # Check if CX gate
520 elif operation.name in ('id', 'u0'):
521 pass
522 elif operation.name in ('CX', 'cx'):
523 qubit0 = operation.qubits[0]
524 qubit1 = operation.qubits[1]
525 gate = cx_gate_matrix()
526 self._add_unitary(gate, [qubit0, qubit1])
527 # Check if reset
528 elif operation.name == 'reset':
529 qubit = operation.qubits[0]
530 self._add_qasm_reset(qubit)
531 # Check if barrier
532 elif operation.name == 'barrier':
533 pass
534 # Check if measure
535 elif operation.name == 'measure':
536 qubit = operation.qubits[0]
537 cmembit = operation.memory[0]
538 cregbit = operation.register[0] if hasattr(operation, 'register') else None
539
540 if self._sample_measure:
541 # If sampling measurements record the qubit and cmembit
542 # for this measurement for later sampling
543 measure_sample_ops.append((qubit, cmembit))
544 else:
545 # If not sampling perform measurement as normal
546 self._add_qasm_measure(qubit, cmembit, cregbit)
547 elif operation.name == 'bfunc':
548 mask = int(operation.mask, 16)
549 relation = operation.relation
550 val = int(operation.val, 16)
551
552 cregbit = operation.register
553 cmembit = operation.memory if hasattr(operation, 'memory') else None
554
555 compared = (self._classical_register & mask) - val
556
557 if relation == '==':
558 outcome = (compared == 0)
559 elif relation == '!=':
560 outcome = (compared != 0)
561 elif relation == '<':
562 outcome = (compared < 0)
563 elif relation == '<=':
564 outcome = (compared <= 0)
565 elif relation == '>':
566 outcome = (compared > 0)
567 elif relation == '>=':
568 outcome = (compared >= 0)
569 else:
570 raise BasicAerError('Invalid boolean function relation.')
571
572 # Store outcome in register and optionally memory slot
573 regbit = 1 << cregbit
574 self._classical_register = \
575 (self._classical_register & (~regbit)) | (int(outcome) << cregbit)
576 if cmembit is not None:
577 membit = 1 << cmembit
578 self._classical_memory = \
579 (self._classical_memory & (~membit)) | (int(outcome) << cmembit)
580 else:
581 backend = self.name()
582 err_msg = '{0} encountered unrecognized operation "{1}"'
583 raise BasicAerError(err_msg.format(backend, operation.name))
584
585 # Add final creg data to memory list
586 if self._number_of_cmembits > 0:
587 if self._sample_measure:
588 # If sampling we generate all shot samples from the final statevector
589 memory = self._add_sample_measure(measure_sample_ops, self._shots)
590 else:
591 # Turn classical_memory (int) into bit string and pad zero for unused cmembits
592 outcome = bin(self._classical_memory)[2:]
593 memory.append(hex(int(outcome, 2)))
594
595 # Add data
596 data = {'counts': dict(Counter(memory))}
597 # Optionally add memory list
598 if self._memory:
599 data['memory'] = memory
600 # Optionally add final statevector
601 if self.SHOW_FINAL_STATE:
602 data['statevector'] = self._get_statevector()
603 # Remove empty counts and memory for statevector simulator
604 if not data['counts']:
605 data.pop('counts')
606 if 'memory' in data and not data['memory']:
607 data.pop('memory')
608 end = time.time()
609 return {'name': experiment.header.name,
610 'seed_simulator': seed_simulator,
611 'shots': self._shots,
612 'data': data,
613 'status': 'DONE',
614 'success': True,
615 'time_taken': (end - start),
616 'header': experiment.header.to_dict()}
617
618 def _validate(self, qobj):
619 """Semantic validations of the qobj which cannot be done via schemas."""
620 n_qubits = qobj.config.n_qubits
621 max_qubits = self.configuration().n_qubits
622 if n_qubits > max_qubits:
623 raise BasicAerError('Number of qubits {} '.format(n_qubits) +
624 'is greater than maximum ({}) '.format(max_qubits) +
625 'for "{}".'.format(self.name()))
626 for experiment in qobj.experiments:
627 name = experiment.header.name
628 if experiment.config.memory_slots == 0:
629 logger.warning('No classical registers in circuit "%s", '
630 'counts will be empty.', name)
631 elif 'measure' not in [op.name for op in experiment.instructions]:
632 logger.warning('No measurements in circuit "%s", '
633 'classical register will remain all zeros.', name)
634
[end of qiskit/providers/basicaer/qasm_simulator.py]
[start of qiskit/providers/basicaer/unitary_simulator.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 # pylint: disable=arguments-differ
16
17 """Contains a Python simulator that returns the unitary of the circuit.
18
19 It simulates a unitary of a quantum circuit that has been compiled to run on
20 the simulator. It is exponential in the number of qubits.
21
22 .. code-block:: python
23
24 UnitarySimulator().run(qobj)
25
26 Where the input is a Qobj object and the output is a BasicAerJob object, which can
27 later be queried for the Result object. The result will contain a 'unitary'
28 data field, which is a 2**n x 2**n complex numpy array representing the
29 circuit's unitary matrix.
30 """
31 import logging
32 import uuid
33 import time
34 from math import log2, sqrt
35 import numpy as np
36 from qiskit.util import local_hardware_info
37 from qiskit.providers.models import QasmBackendConfiguration
38 from qiskit.providers import BaseBackend
39 from qiskit.providers.basicaer.basicaerjob import BasicAerJob
40 from qiskit.result import Result
41 from .exceptions import BasicAerError
42 from .basicaertools import single_gate_matrix
43 from .basicaertools import cx_gate_matrix
44 from .basicaertools import einsum_matmul_index
45
46 logger = logging.getLogger(__name__)
47
48
49 # TODO add ["status"] = 'DONE', 'ERROR' especially for empty circuit error
50 # does not show up
51
52
53 class UnitarySimulatorPy(BaseBackend):
54 """Python implementation of a unitary simulator."""
55
56 MAX_QUBITS_MEMORY = int(log2(sqrt(local_hardware_info()['memory'] * (1024 ** 3) / 16)))
57
58 DEFAULT_CONFIGURATION = {
59 'backend_name': 'unitary_simulator',
60 'backend_version': '1.0.0',
61 'n_qubits': min(24, MAX_QUBITS_MEMORY),
62 'url': 'https://github.com/Qiskit/qiskit-terra',
63 'simulator': True,
64 'local': True,
65 'conditional': False,
66 'open_pulse': False,
67 'memory': False,
68 'max_shots': 65536,
69 'coupling_map': None,
70 'description': 'A python simulator for unitary matrix corresponding to a circuit',
71 'basis_gates': ['u1', 'u2', 'u3', 'cx', 'id', 'unitary'],
72 'gates': [
73 {
74 'name': 'u1',
75 'parameters': ['lambda'],
76 'qasm_def': 'gate u1(lambda) q { U(0,0,lambda) q; }'
77 },
78 {
79 'name': 'u2',
80 'parameters': ['phi', 'lambda'],
81 'qasm_def': 'gate u2(phi,lambda) q { U(pi/2,phi,lambda) q; }'
82 },
83 {
84 'name': 'u3',
85 'parameters': ['theta', 'phi', 'lambda'],
86 'qasm_def': 'gate u3(theta,phi,lambda) q { U(theta,phi,lambda) q; }'
87 },
88 {
89 'name': 'cx',
90 'parameters': ['c', 't'],
91 'qasm_def': 'gate cx c,t { CX c,t; }'
92 },
93 {
94 'name': 'id',
95 'parameters': ['a'],
96 'qasm_def': 'gate id a { U(0,0,0) a; }'
97 },
98 {
99 'name': 'unitary',
100 'parameters': ['matrix'],
101 'qasm_def': 'unitary(matrix) q1, q2,...'
102 }
103 ]
104 }
105
106 DEFAULT_OPTIONS = {
107 "initial_unitary": None,
108 "chop_threshold": 1e-15
109 }
110
111 def __init__(self, configuration=None, provider=None):
112 super().__init__(configuration=(
113 configuration or QasmBackendConfiguration.from_dict(self.DEFAULT_CONFIGURATION)),
114 provider=provider)
115
116 # Define attributes inside __init__.
117 self._unitary = None
118 self._number_of_qubits = 0
119 self._initial_unitary = None
120 self._chop_threshold = 1e-15
121
122 def _add_unitary(self, gate, qubits):
123 """Apply an N-qubit unitary matrix.
124
125 Args:
126 gate (matrix_like): an N-qubit unitary matrix
127 qubits (list): the list of N-qubits.
128 """
129 # Get the number of qubits
130 num_qubits = len(qubits)
131 # Compute einsum index string for 1-qubit matrix multiplication
132 indexes = einsum_matmul_index(qubits, self._number_of_qubits)
133 # Convert to complex rank-2N tensor
134 gate_tensor = np.reshape(np.array(gate, dtype=complex),
135 num_qubits * [2, 2])
136 # Apply matrix multiplication
137 self._unitary = np.einsum(indexes, gate_tensor, self._unitary,
138 dtype=complex, casting='no')
139
140 def _validate_initial_unitary(self):
141 """Validate an initial unitary matrix"""
142 # If initial unitary isn't set we don't need to validate
143 if self._initial_unitary is None:
144 return
145 # Check unitary is correct length for number of qubits
146 shape = np.shape(self._initial_unitary)
147 required_shape = (2 ** self._number_of_qubits,
148 2 ** self._number_of_qubits)
149 if shape != required_shape:
150 raise BasicAerError('initial unitary is incorrect shape: ' +
151 '{} != 2 ** {}'.format(shape, required_shape))
152
153 def _set_options(self, qobj_config=None, backend_options=None):
154 """Set the backend options for all experiments in a qobj"""
155 # Reset default options
156 self._initial_unitary = self.DEFAULT_OPTIONS["initial_unitary"]
157 self._chop_threshold = self.DEFAULT_OPTIONS["chop_threshold"]
158 if backend_options is None:
159 backend_options = {}
160
161 # Check for custom initial statevector in backend_options first,
162 # then config second
163 if 'initial_unitary' in backend_options:
164 self._initial_unitary = np.array(backend_options['initial_unitary'],
165 dtype=complex)
166 elif hasattr(qobj_config, 'initial_unitary'):
167 self._initial_unitary = np.array(qobj_config.initial_unitary,
168 dtype=complex)
169 if self._initial_unitary is not None:
170 # Check the initial unitary is actually unitary
171 shape = np.shape(self._initial_unitary)
172 if len(shape) != 2 or shape[0] != shape[1]:
173 raise BasicAerError("initial unitary is not a square matrix")
174 iden = np.eye(len(self._initial_unitary))
175 u_dagger_u = np.dot(self._initial_unitary.T.conj(),
176 self._initial_unitary)
177 norm = np.linalg.norm(u_dagger_u - iden)
178 if round(norm, 10) != 0:
179 raise BasicAerError("initial unitary is not unitary")
180 # Check the initial statevector is normalized
181
182 # Check for custom chop threshold
183 # Replace with custom options
184 if 'chop_threshold' in backend_options:
185 self._chop_threshold = backend_options['chop_threshold']
186 elif hasattr(qobj_config, 'chop_threshold'):
187 self._chop_threshold = qobj_config.chop_threshold
188
189 def _initialize_unitary(self):
190 """Set the initial unitary for simulation"""
191 self._validate_initial_unitary()
192 if self._initial_unitary is None:
193 # Set to identity matrix
194 self._unitary = np.eye(2 ** self._number_of_qubits,
195 dtype=complex)
196 else:
197 self._unitary = self._initial_unitary.copy()
198 # Reshape to rank-N tensor
199 self._unitary = np.reshape(self._unitary,
200 self._number_of_qubits * [2, 2])
201
202 def _get_unitary(self):
203 """Return the current unitary"""
204 unitary = np.reshape(self._unitary, 2 * [2 ** self._number_of_qubits])
205 unitary[abs(unitary) < self._chop_threshold] = 0.0
206 return unitary
207
208 def run(self, qobj, backend_options=None):
209 """Run qobj asynchronously.
210
211 Args:
212 qobj (Qobj): payload of the experiment
213 backend_options (dict): backend options
214
215 Returns:
216 BasicAerJob: derived from BaseJob
217
218 Additional Information::
219
220 backend_options: Is a dict of options for the backend. It may contain
221 * "initial_unitary": matrix_like
222 * "chop_threshold": double
223
224 The "initial_unitary" option specifies a custom initial unitary
225 matrix for the simulator to be used instead of the identity
226 matrix. This size of this matrix must be correct for the number
227 of qubits inall experiments in the qobj.
228
229 The "chop_threshold" option specifies a truncation value for
230 setting small values to zero in the output unitary. The default
231 value is 1e-15.
232
233 Example::
234
235 backend_options = {
236 "initial_unitary": np.array([[1, 0, 0, 0],
237 [0, 0, 0, 1],
238 [0, 0, 1, 0],
239 [0, 1, 0, 0]])
240 "chop_threshold": 1e-15
241 }
242 """
243 self._set_options(qobj_config=qobj.config,
244 backend_options=backend_options)
245 job_id = str(uuid.uuid4())
246 job = BasicAerJob(self, job_id, self._run_job, qobj)
247 job.submit()
248 return job
249
250 def _run_job(self, job_id, qobj):
251 """Run experiments in qobj.
252
253 Args:
254 job_id (str): unique id for the job.
255 qobj (Qobj): job description
256
257 Returns:
258 Result: Result object
259 """
260 self._validate(qobj)
261 result_list = []
262 start = time.time()
263 for experiment in qobj.experiments:
264 result_list.append(self.run_experiment(experiment))
265 end = time.time()
266 result = {'backend_name': self.name(),
267 'backend_version': self._configuration.backend_version,
268 'qobj_id': qobj.qobj_id,
269 'job_id': job_id,
270 'results': result_list,
271 'status': 'COMPLETED',
272 'success': True,
273 'time_taken': (end - start),
274 'header': qobj.header.to_dict()}
275
276 return Result.from_dict(result)
277
278 def run_experiment(self, experiment):
279 """Run an experiment (circuit) and return a single experiment result.
280
281 Args:
282 experiment (QobjExperiment): experiment from qobj experiments list
283
284 Returns:
285 dict: A result dictionary which looks something like::
286
287 {
288 "name": name of this experiment (obtained from qobj.experiment header)
289 "seed": random seed used for simulation
290 "shots": number of shots used in the simulation
291 "data":
292 {
293 "unitary": [[[0.0, 0.0], [1.0, 0.0]],
294 [[1.0, 0.0], [0.0, 0.0]]]
295 },
296 "status": status string for the simulation
297 "success": boolean
298 "time taken": simulation time of this single experiment
299 }
300
301 Raises:
302 BasicAerError: if the number of qubits in the circuit is greater than 24.
303 Note that the practical qubit limit is much lower than 24.
304 """
305 start = time.time()
306 self._number_of_qubits = experiment.header.n_qubits
307
308 # Validate the dimension of initial unitary if set
309 self._validate_initial_unitary()
310 self._initialize_unitary()
311
312 for operation in experiment.instructions:
313 if operation.name == 'unitary':
314 qubits = operation.qubits
315 gate = operation.params[0]
316 self._add_unitary(gate, qubits)
317 # Check if single gate
318 elif operation.name in ('U', 'u1', 'u2', 'u3'):
319 params = getattr(operation, 'params', None)
320 qubit = operation.qubits[0]
321 gate = single_gate_matrix(operation.name, params)
322 self._add_unitary(gate, [qubit])
323 elif operation.name in ('id', 'u0'):
324 pass
325 # Check if CX gate
326 elif operation.name in ('CX', 'cx'):
327 qubit0 = operation.qubits[0]
328 qubit1 = operation.qubits[1]
329 gate = cx_gate_matrix()
330 self._add_unitary(gate, [qubit0, qubit1])
331 # Check if barrier
332 elif operation.name == 'barrier':
333 pass
334 else:
335 backend = self.name()
336 err_msg = '{0} encountered unrecognized operation "{1}"'
337 raise BasicAerError(err_msg.format(backend, operation.name))
338 # Add final state to data
339 data = {'unitary': self._get_unitary()}
340 end = time.time()
341 return {'name': experiment.header.name,
342 'shots': 1,
343 'data': data,
344 'status': 'DONE',
345 'success': True,
346 'time_taken': (end - start),
347 'header': experiment.header.to_dict()}
348
349 def _validate(self, qobj):
350 """Semantic validations of the qobj which cannot be done via schemas.
351 Some of these may later move to backend schemas.
352 1. No shots
353 2. No measurements in the middle
354 """
355 n_qubits = qobj.config.n_qubits
356 max_qubits = self.configuration().n_qubits
357 if n_qubits > max_qubits:
358 raise BasicAerError('Number of qubits {} '.format(n_qubits) +
359 'is greater than maximum ({}) '.format(max_qubits) +
360 'for "{}".'.format(self.name()))
361 if hasattr(qobj.config, 'shots') and qobj.config.shots != 1:
362 logger.info('"%s" only supports 1 shot. Setting shots=1.',
363 self.name())
364 qobj.config.shots = 1
365 for experiment in qobj.experiments:
366 name = experiment.header.name
367 if getattr(experiment.config, 'shots', 1) != 1:
368 logger.info('"%s" only supports 1 shot. '
369 'Setting shots=1 for circuit "%s".',
370 self.name(), name)
371 experiment.config.shots = 1
372 for operation in experiment.instructions:
373 if operation.name in ['measure', 'reset']:
374 raise BasicAerError('Unsupported "%s" instruction "%s" ' +
375 'in circuit "%s" ', self.name(),
376 operation.name, name)
377
[end of qiskit/providers/basicaer/unitary_simulator.py]
[start of qiskit/quantum_info/operators/operator.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017, 2019.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 """
16 Matrix Operator class.
17 """
18
19 import copy
20 import re
21 from numbers import Number
22
23 import numpy as np
24
25 from qiskit.circuit.quantumcircuit import QuantumCircuit
26 from qiskit.circuit.instruction import Instruction
27 from qiskit.circuit.library.standard_gates import IGate, XGate, YGate, ZGate, HGate, SGate, TGate
28 from qiskit.exceptions import QiskitError
29 from qiskit.quantum_info.operators.predicates import is_unitary_matrix, matrix_equal
30 from qiskit.quantum_info.operators.base_operator import BaseOperator
31
32
33 class Operator(BaseOperator):
34 r"""Matrix operator class
35
36 This represents a matrix operator :math:`M` that will
37 :meth:`~Statevector.evolve` a :class:`Statevector` :math:`|\psi\rangle`
38 by matrix-vector multiplication
39
40 .. math::
41
42 |\psi\rangle \mapsto M|\psi\rangle,
43
44 and will :meth:`~DensityMatrix.evolve` a :class:`DensityMatrix` :math:`\rho`
45 by left and right multiplication
46
47 .. math::
48
49 \rho \mapsto M \rho M^\dagger.
50 """
51
52 def __init__(self, data, input_dims=None, output_dims=None):
53 """Initialize an operator object.
54
55 Args:
56 data (QuantumCircuit or
57 Instruction or
58 BaseOperator or
59 matrix): data to initialize operator.
60 input_dims (tuple): the input subsystem dimensions.
61 [Default: None]
62 output_dims (tuple): the output subsystem dimensions.
63 [Default: None]
64
65 Raises:
66 QiskitError: if input data cannot be initialized as an operator.
67
68 Additional Information:
69 If the input or output dimensions are None, they will be
70 automatically determined from the input data. If the input data is
71 a Numpy array of shape (2**N, 2**N) qubit systems will be used. If
72 the input operator is not an N-qubit operator, it will assign a
73 single subsystem with dimension specified by the shape of the input.
74 """
75 if isinstance(data, (list, np.ndarray)):
76 # Default initialization from list or numpy array matrix
77 self._data = np.asarray(data, dtype=complex)
78 elif isinstance(data, (QuantumCircuit, Instruction)):
79 # If the input is a Terra QuantumCircuit or Instruction we
80 # perform a simulation to construct the unitary operator.
81 # This will only work if the circuit or instruction can be
82 # defined in terms of unitary gate instructions which have a
83 # 'to_matrix' method defined. Any other instructions such as
84 # conditional gates, measure, or reset will cause an
85 # exception to be raised.
86 self._data = self._init_instruction(data).data
87 elif hasattr(data, 'to_operator'):
88 # If the data object has a 'to_operator' attribute this is given
89 # higher preference than the 'to_matrix' method for initializing
90 # an Operator object.
91 data = data.to_operator()
92 self._data = data.data
93 if input_dims is None:
94 input_dims = data._input_dims
95 if output_dims is None:
96 output_dims = data._output_dims
97 elif hasattr(data, 'to_matrix'):
98 # If no 'to_operator' attribute exists we next look for a
99 # 'to_matrix' attribute to a matrix that will be cast into
100 # a complex numpy matrix.
101 self._array = np.asarray(data.to_matrix(), dtype=complex)
102 else:
103 raise QiskitError("Invalid input data format for Operator")
104 # Determine input and output dimensions
105 dout, din = self._data.shape
106 output_dims = self._automatic_dims(output_dims, dout)
107 input_dims = self._automatic_dims(input_dims, din)
108 super().__init__(input_dims, output_dims)
109
110 def __repr__(self):
111 prefix = 'Operator('
112 pad = len(prefix) * ' '
113 return '{}{},\n{}input_dims={}, output_dims={})'.format(
114 prefix, np.array2string(
115 self.data, separator=', ', prefix=prefix),
116 pad, self._input_dims, self._output_dims)
117
118 def __eq__(self, other):
119 """Test if two Operators are equal."""
120 if not super().__eq__(other):
121 return False
122 return np.allclose(
123 self.data, other.data, rtol=self.rtol, atol=self.atol)
124
125 @property
126 def data(self):
127 """Return data."""
128 return self._data
129
130 @classmethod
131 def from_label(cls, label):
132 """Return a tensor product of single-qubit operators.
133
134 Args:
135 label (string): single-qubit operator string.
136
137 Returns:
138 Operator: The N-qubit operator.
139
140 Raises:
141 QiskitError: if the label contains invalid characters, or the
142 length of the label is larger than an explicitly
143 specified num_qubits.
144
145 Additional Information:
146 The labels correspond to the single-qubit matrices:
147 'I': [[1, 0], [0, 1]]
148 'X': [[0, 1], [1, 0]]
149 'Y': [[0, -1j], [1j, 0]]
150 'Z': [[1, 0], [0, -1]]
151 'H': [[1, 1], [1, -1]] / sqrt(2)
152 'S': [[1, 0], [0 , 1j]]
153 'T': [[1, 0], [0, (1+1j) / sqrt(2)]]
154 '0': [[1, 0], [0, 0]]
155 '1': [[0, 0], [0, 1]]
156 '+': [[0.5, 0.5], [0.5 , 0.5]]
157 '-': [[0.5, -0.5], [-0.5 , 0.5]]
158 'r': [[0.5, -0.5j], [0.5j , 0.5]]
159 'l': [[0.5, 0.5j], [-0.5j , 0.5]]
160 """
161 # Check label is valid
162 label_mats = {
163 'I': IGate().to_matrix(),
164 'X': XGate().to_matrix(),
165 'Y': YGate().to_matrix(),
166 'Z': ZGate().to_matrix(),
167 'H': HGate().to_matrix(),
168 'S': SGate().to_matrix(),
169 'T': TGate().to_matrix(),
170 '0': np.array([[1, 0], [0, 0]], dtype=complex),
171 '1': np.array([[0, 0], [0, 1]], dtype=complex),
172 '+': np.array([[0.5, 0.5], [0.5, 0.5]], dtype=complex),
173 '-': np.array([[0.5, -0.5], [-0.5, 0.5]], dtype=complex),
174 'r': np.array([[0.5, -0.5j], [0.5j, 0.5]], dtype=complex),
175 'l': np.array([[0.5, 0.5j], [-0.5j, 0.5]], dtype=complex),
176 }
177 if re.match(r'^[IXYZHST01rl\-+]+$', label) is None:
178 raise QiskitError('Label contains invalid characters.')
179 # Initialize an identity matrix and apply each gate
180 num_qubits = len(label)
181 op = Operator(np.eye(2 ** num_qubits, dtype=complex))
182 for qubit, char in enumerate(reversed(label)):
183 if char != 'I':
184 op = op.compose(label_mats[char], qargs=[qubit])
185 return op
186
187 def is_unitary(self, atol=None, rtol=None):
188 """Return True if operator is a unitary matrix."""
189 if atol is None:
190 atol = self.atol
191 if rtol is None:
192 rtol = self.rtol
193 return is_unitary_matrix(self._data, rtol=rtol, atol=atol)
194
195 def to_operator(self):
196 """Convert operator to matrix operator class"""
197 return self
198
199 def to_instruction(self):
200 """Convert to a UnitaryGate instruction."""
201 # pylint: disable=cyclic-import
202 from qiskit.extensions.unitary import UnitaryGate
203 return UnitaryGate(self.data)
204
205 def conjugate(self):
206 """Return the conjugate of the operator."""
207 # Make a shallow copy and update array
208 ret = copy.copy(self)
209 ret._data = np.conj(self._data)
210 return ret
211
212 def transpose(self):
213 """Return the transpose of the operator."""
214 # Make a shallow copy and update array
215 ret = copy.copy(self)
216 ret._data = np.transpose(self._data)
217 # Swap input and output dimensions
218 ret._set_dims(self._output_dims, self._input_dims)
219 return ret
220
221 def compose(self, other, qargs=None, front=False):
222 """Return the composed operator.
223
224 Args:
225 other (Operator): an operator object.
226 qargs (list or None): a list of subsystem positions to apply
227 other on. If None apply on all
228 subsystems [default: None].
229 front (bool): If True compose using right operator multiplication,
230 instead of left multiplication [default: False].
231
232 Returns:
233 Operator: The operator self @ other.
234
235 Raise:
236 QiskitError: if operators have incompatible dimensions for
237 composition.
238
239 Additional Information:
240 Composition (``@``) is defined as `left` matrix multiplication for
241 matrix operators. That is that ``A @ B`` is equal to ``B * A``.
242 Setting ``front=True`` returns `right` matrix multiplication
243 ``A * B`` and is equivalent to the :meth:`dot` method.
244 """
245 if qargs is None:
246 qargs = getattr(other, 'qargs', None)
247 if not isinstance(other, Operator):
248 other = Operator(other)
249 # Validate dimensions are compatible and return the composed
250 # operator dimensions
251 input_dims, output_dims = self._get_compose_dims(
252 other, qargs, front)
253
254 # Full composition of operators
255 if qargs is None:
256 if front:
257 # Composition self * other
258 data = np.dot(self._data, other.data)
259 else:
260 # Composition other * self
261 data = np.dot(other.data, self._data)
262 return Operator(data, input_dims, output_dims)
263
264 # Compose with other on subsystem
265 if front:
266 num_indices = len(self._input_dims)
267 shift = len(self._output_dims)
268 right_mul = True
269 else:
270 num_indices = len(self._output_dims)
271 shift = 0
272 right_mul = False
273
274 # Reshape current matrix
275 # Note that we must reverse the subsystem dimension order as
276 # qubit 0 corresponds to the right-most position in the tensor
277 # product, which is the last tensor wire index.
278 tensor = np.reshape(self.data, self._shape)
279 mat = np.reshape(other.data, other._shape)
280 indices = [num_indices - 1 - qubit for qubit in qargs]
281 final_shape = [np.product(output_dims), np.product(input_dims)]
282 data = np.reshape(
283 Operator._einsum_matmul(tensor, mat, indices, shift, right_mul),
284 final_shape)
285 return Operator(data, input_dims, output_dims)
286
287 def dot(self, other, qargs=None):
288 """Return the right multiplied operator self * other.
289
290 Args:
291 other (Operator): an operator object.
292 qargs (list or None): a list of subsystem positions to apply
293 other on. If None apply on all
294 subsystems [default: None].
295
296 Returns:
297 Operator: The operator self * other.
298
299 Raises:
300 QiskitError: if other cannot be converted to an Operator or has
301 incompatible dimensions.
302 """
303 return super().dot(other, qargs=qargs)
304
305 def power(self, n):
306 """Return the matrix power of the operator.
307
308 Args:
309 n (int): the power to raise the matrix to.
310
311 Returns:
312 BaseOperator: the n-times composed operator.
313
314 Raises:
315 QiskitError: if the input and output dimensions of the operator
316 are not equal, or the power is not a positive integer.
317 """
318 if not isinstance(n, int):
319 raise QiskitError("Can only take integer powers of Operator.")
320 if self.input_dims() != self.output_dims():
321 raise QiskitError("Can only power with input_dims = output_dims.")
322 # Override base class power so we can implement more efficiently
323 # using Numpy.matrix_power
324 ret = copy.copy(self)
325 ret._data = np.linalg.matrix_power(self.data, n)
326 return ret
327
328 def tensor(self, other):
329 """Return the tensor product operator self ⊗ other.
330
331 Args:
332 other (Operator): a operator subclass object.
333
334 Returns:
335 Operator: the tensor product operator self ⊗ other.
336
337 Raises:
338 QiskitError: if other cannot be converted to an operator.
339 """
340 if not isinstance(other, Operator):
341 other = Operator(other)
342 input_dims = other.input_dims() + self.input_dims()
343 output_dims = other.output_dims() + self.output_dims()
344 data = np.kron(self._data, other._data)
345 return Operator(data, input_dims, output_dims)
346
347 def expand(self, other):
348 """Return the tensor product operator other ⊗ self.
349
350 Args:
351 other (Operator): an operator object.
352
353 Returns:
354 Operator: the tensor product operator other ⊗ self.
355
356 Raises:
357 QiskitError: if other cannot be converted to an operator.
358 """
359 if not isinstance(other, Operator):
360 other = Operator(other)
361 input_dims = self.input_dims() + other.input_dims()
362 output_dims = self.output_dims() + other.output_dims()
363 data = np.kron(other._data, self._data)
364 return Operator(data, input_dims, output_dims)
365
366 def _add(self, other, qargs=None):
367 """Return the operator self + other.
368
369 If ``qargs`` are specified the other operator will be added
370 assuming it is identity on all other subsystems.
371
372 Args:
373 other (Operator): an operator object.
374 qargs (None or list): optional subsystems to add on
375 (Default: None)
376
377 Returns:
378 Operator: the operator self + other.
379
380 Raises:
381 QiskitError: if other is not an operator, or has incompatible
382 dimensions.
383 """
384 # pylint: disable=import-outside-toplevel, cyclic-import
385 from qiskit.quantum_info.operators.scalar_op import ScalarOp
386
387 if qargs is None:
388 qargs = getattr(other, 'qargs', None)
389
390 if not isinstance(other, Operator):
391 other = Operator(other)
392
393 self._validate_add_dims(other, qargs)
394 other = ScalarOp._pad_with_identity(self, other, qargs)
395
396 ret = copy.copy(self)
397 ret._data = self.data + other.data
398 return ret
399
400 def _multiply(self, other):
401 """Return the operator self * other.
402
403 Args:
404 other (complex): a complex number.
405
406 Returns:
407 Operator: the operator other * self.
408
409 Raises:
410 QiskitError: if other is not a valid complex number.
411 """
412 if not isinstance(other, Number):
413 raise QiskitError("other is not a number")
414 ret = copy.copy(self)
415 ret._data = other * self._data
416 return ret
417
418 def equiv(self, other, rtol=None, atol=None):
419 """Return True if operators are equivalent up to global phase.
420
421 Args:
422 other (Operator): an operator object.
423 rtol (float): relative tolerance value for comparison.
424 atol (float): absolute tolerance value for comparison.
425
426 Returns:
427 bool: True if operators are equivalent up to global phase.
428 """
429 if not isinstance(other, Operator):
430 try:
431 other = Operator(other)
432 except QiskitError:
433 return False
434 if self.dim != other.dim:
435 return False
436 if atol is None:
437 atol = self.atol
438 if rtol is None:
439 rtol = self.rtol
440 return matrix_equal(self.data, other.data, ignore_phase=True,
441 rtol=rtol, atol=atol)
442
443 @property
444 def _shape(self):
445 """Return the tensor shape of the matrix operator"""
446 return tuple(reversed(self.output_dims())) + tuple(
447 reversed(self.input_dims()))
448
449 @classmethod
450 def _einsum_matmul(cls, tensor, mat, indices, shift=0, right_mul=False):
451 """Perform a contraction using Numpy.einsum
452
453 Args:
454 tensor (np.array): a vector or matrix reshaped to a rank-N tensor.
455 mat (np.array): a matrix reshaped to a rank-2M tensor.
456 indices (list): tensor indices to contract with mat.
457 shift (int): shift for indices of tensor to contract [Default: 0].
458 right_mul (bool): if True right multiply tensor by mat
459 (else left multiply) [Default: False].
460
461 Returns:
462 Numpy.ndarray: the matrix multiplied rank-N tensor.
463
464 Raises:
465 QiskitError: if mat is not an even rank tensor.
466 """
467 rank = tensor.ndim
468 rank_mat = mat.ndim
469 if rank_mat % 2 != 0:
470 raise QiskitError(
471 "Contracted matrix must have an even number of indices.")
472 # Get einsum indices for tensor
473 indices_tensor = list(range(rank))
474 for j, index in enumerate(indices):
475 indices_tensor[index + shift] = rank + j
476 # Get einsum indices for mat
477 mat_contract = list(reversed(range(rank, rank + len(indices))))
478 mat_free = [index + shift for index in reversed(indices)]
479 if right_mul:
480 indices_mat = mat_contract + mat_free
481 else:
482 indices_mat = mat_free + mat_contract
483 return np.einsum(tensor, indices_tensor, mat, indices_mat)
484
485 @classmethod
486 def _init_instruction(cls, instruction):
487 """Convert a QuantumCircuit or Instruction to an Operator."""
488 # Convert circuit to an instruction
489 if isinstance(instruction, QuantumCircuit):
490 instruction = instruction.to_instruction()
491 # Initialize an identity operator of the correct size of the circuit
492 op = Operator(np.eye(2 ** instruction.num_qubits))
493 op._append_instruction(instruction)
494 return op
495
496 @classmethod
497 def _instruction_to_matrix(cls, obj):
498 """Return Operator for instruction if defined or None otherwise."""
499 if not isinstance(obj, Instruction):
500 raise QiskitError('Input is not an instruction.')
501 mat = None
502 if hasattr(obj, 'to_matrix'):
503 # If instruction is a gate first we see if it has a
504 # `to_matrix` definition and if so use that.
505 try:
506 mat = obj.to_matrix()
507 except QiskitError:
508 pass
509 return mat
510
511 def _append_instruction(self, obj, qargs=None):
512 """Update the current Operator by apply an instruction."""
513 mat = self._instruction_to_matrix(obj)
514 if mat is not None:
515 # Perform the composition and inplace update the current state
516 # of the operator
517 op = self.compose(mat, qargs=qargs)
518 self._data = op.data
519 else:
520 # If the instruction doesn't have a matrix defined we use its
521 # circuit decomposition definition if it exists, otherwise we
522 # cannot compose this gate and raise an error.
523 if obj.definition is None:
524 raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
525 for instr, qregs, cregs in obj.definition:
526 if cregs:
527 raise QiskitError(
528 'Cannot apply instruction with classical registers: {}'.format(
529 instr.name))
530 # Get the integer position of the flat register
531 if qargs is None:
532 new_qargs = [tup.index for tup in qregs]
533 else:
534 new_qargs = [qargs[tup.index] for tup in qregs]
535 self._append_instruction(instr, qargs=new_qargs)
536
[end of qiskit/quantum_info/operators/operator.py]
[start of qiskit/quantum_info/states/densitymatrix.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017, 2019.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 """
16 DensityMatrix quantum state class.
17 """
18
19 import warnings
20 from numbers import Number
21 import numpy as np
22
23 from qiskit.circuit.quantumcircuit import QuantumCircuit
24 from qiskit.circuit.instruction import Instruction
25 from qiskit.exceptions import QiskitError
26 from qiskit.quantum_info.states.quantum_state import QuantumState
27 from qiskit.quantum_info.operators.operator import Operator
28 from qiskit.quantum_info.operators.scalar_op import ScalarOp
29 from qiskit.quantum_info.operators.predicates import is_hermitian_matrix
30 from qiskit.quantum_info.operators.predicates import is_positive_semidefinite_matrix
31 from qiskit.quantum_info.operators.channel.quantum_channel import QuantumChannel
32 from qiskit.quantum_info.operators.channel.superop import SuperOp
33 from qiskit.quantum_info.states.statevector import Statevector
34
35
36 class DensityMatrix(QuantumState):
37 """DensityMatrix class"""
38
39 def __init__(self, data, dims=None):
40 """Initialize a density matrix object.
41
42 Args:
43 data (matrix_like or vector_like): a density matrix or
44 statevector. If a vector the density matrix is constructed
45 as the projector of that vector.
46 dims (int or tuple or list): Optional. The subsystem dimension
47 of the state (See additional information).
48
49 Raises:
50 QiskitError: if input data is not valid.
51
52 Additional Information:
53 The ``dims`` kwarg can be None, an integer, or an iterable of
54 integers.
55
56 * ``Iterable`` -- the subsystem dimensions are the values in the list
57 with the total number of subsystems given by the length of the list.
58
59 * ``Int`` or ``None`` -- the leading dimension of the input matrix
60 specifies the total dimension of the density matrix. If it is a
61 power of two the state will be initialized as an N-qubit state.
62 If it is not a power of two the state will have a single
63 d-dimensional subsystem.
64 """
65 if isinstance(data, (list, np.ndarray)):
66 # Finally we check if the input is a raw matrix in either a
67 # python list or numpy array format.
68 self._data = np.asarray(data, dtype=complex)
69 elif hasattr(data, 'to_operator'):
70 # If the data object has a 'to_operator' attribute this is given
71 # higher preference than the 'to_matrix' method for initializing
72 # an Operator object.
73 op = data.to_operator()
74 self._data = op.data
75 if dims is None:
76 dims = op._output_dims
77 elif hasattr(data, 'to_matrix'):
78 # If no 'to_operator' attribute exists we next look for a
79 # 'to_matrix' attribute to a matrix that will be cast into
80 # a complex numpy matrix.
81 self._data = np.asarray(data.to_matrix(), dtype=complex)
82 else:
83 raise QiskitError("Invalid input data format for DensityMatrix")
84 # Convert statevector into a density matrix
85 ndim = self._data.ndim
86 shape = self._data.shape
87 if ndim == 2 and shape[0] == shape[1]:
88 pass # We good
89 elif ndim == 1:
90 self._data = np.outer(self._data, np.conj(self._data))
91 elif ndim == 2 and shape[1] == 1:
92 self._data = np.reshape(self._data, shape[0])
93 shape = self._data.shape
94 else:
95 raise QiskitError(
96 "Invalid DensityMatrix input: not a square matrix.")
97 super().__init__(self._automatic_dims(dims, shape[0]))
98
99 def __eq__(self, other):
100 return super().__eq__(other) and np.allclose(
101 self._data, other._data, rtol=self.rtol, atol=self.atol)
102
103 def __repr__(self):
104 prefix = 'DensityMatrix('
105 pad = len(prefix) * ' '
106 return '{}{},\n{}dims={})'.format(
107 prefix, np.array2string(
108 self._data, separator=', ', prefix=prefix),
109 pad, self._dims)
110
111 @property
112 def data(self):
113 """Return data."""
114 return self._data
115
116 def is_valid(self, atol=None, rtol=None):
117 """Return True if trace 1 and positive semidefinite."""
118 if atol is None:
119 atol = self.atol
120 if rtol is None:
121 rtol = self.rtol
122 # Check trace == 1
123 if not np.allclose(self.trace(), 1, rtol=rtol, atol=atol):
124 return False
125 # Check Hermitian
126 if not is_hermitian_matrix(self.data, rtol=rtol, atol=atol):
127 return False
128 # Check positive semidefinite
129 return is_positive_semidefinite_matrix(self.data, rtol=rtol, atol=atol)
130
131 def to_operator(self):
132 """Convert to Operator"""
133 dims = self.dims()
134 return Operator(self.data, input_dims=dims, output_dims=dims)
135
136 def conjugate(self):
137 """Return the conjugate of the density matrix."""
138 return DensityMatrix(np.conj(self.data), dims=self.dims())
139
140 def trace(self):
141 """Return the trace of the density matrix."""
142 return np.trace(self.data)
143
144 def purity(self):
145 """Return the purity of the quantum state."""
146 # For a valid statevector the purity is always 1, however if we simply
147 # have an arbitrary vector (not correctly normalized) then the
148 # purity is equivalent to the trace squared:
149 # P(|psi>) = Tr[|psi><psi|psi><psi|] = |<psi|psi>|^2
150 return np.trace(np.dot(self.data, self.data))
151
152 def tensor(self, other):
153 """Return the tensor product state self ⊗ other.
154
155 Args:
156 other (DensityMatrix): a quantum state object.
157
158 Returns:
159 DensityMatrix: the tensor product operator self ⊗ other.
160
161 Raises:
162 QiskitError: if other is not a quantum state.
163 """
164 if not isinstance(other, DensityMatrix):
165 other = DensityMatrix(other)
166 dims = other.dims() + self.dims()
167 data = np.kron(self._data, other._data)
168 return DensityMatrix(data, dims)
169
170 def expand(self, other):
171 """Return the tensor product state other ⊗ self.
172
173 Args:
174 other (DensityMatrix): a quantum state object.
175
176 Returns:
177 DensityMatrix: the tensor product state other ⊗ self.
178
179 Raises:
180 QiskitError: if other is not a quantum state.
181 """
182 if not isinstance(other, DensityMatrix):
183 other = DensityMatrix(other)
184 dims = self.dims() + other.dims()
185 data = np.kron(other._data, self._data)
186 return DensityMatrix(data, dims)
187
188 def _add(self, other):
189 """Return the linear combination self + other.
190
191 Args:
192 other (DensityMatrix): a quantum state object.
193
194 Returns:
195 DensityMatrix: the linear combination self + other.
196
197 Raises:
198 QiskitError: if other is not a quantum state, or has
199 incompatible dimensions.
200 """
201 if not isinstance(other, DensityMatrix):
202 other = DensityMatrix(other)
203 if self.dim != other.dim:
204 raise QiskitError("other DensityMatrix has different dimensions.")
205 return DensityMatrix(self.data + other.data, self.dims())
206
207 def _multiply(self, other):
208 """Return the scalar multiplied state other * self.
209
210 Args:
211 other (complex): a complex number.
212
213 Returns:
214 DensityMatrix: the scalar multiplied state other * self.
215
216 Raises:
217 QiskitError: if other is not a valid complex number.
218 """
219 if not isinstance(other, Number):
220 raise QiskitError("other is not a number")
221 return DensityMatrix(other * self.data, self.dims())
222
223 def evolve(self, other, qargs=None):
224 """Evolve a quantum state by an operator.
225
226 Args:
227 other (Operator or QuantumChannel
228 or Instruction or Circuit): The operator to evolve by.
229 qargs (list): a list of QuantumState subsystem positions to apply
230 the operator on.
231
232 Returns:
233 QuantumState: the output quantum state.
234
235 Raises:
236 QiskitError: if the operator dimension does not match the
237 specified QuantumState subsystem dimensions.
238 """
239 if qargs is None:
240 qargs = getattr(other, 'qargs', None)
241
242 # Evolution by a circuit or instruction
243 if isinstance(other, (QuantumCircuit, Instruction)):
244 return self._evolve_instruction(other, qargs=qargs)
245
246 # Evolution by a QuantumChannel
247 if hasattr(other, 'to_quantumchannel'):
248 return other.to_quantumchannel()._evolve(self, qargs=qargs)
249 if isinstance(other, QuantumChannel):
250 return other._evolve(self, qargs=qargs)
251
252 # Unitary evolution by an Operator
253 if not isinstance(other, Operator):
254 other = Operator(other)
255 return self._evolve_operator(other, qargs=qargs)
256
257 def probabilities(self, qargs=None, decimals=None):
258 """Return the subsystem measurement probability vector.
259
260 Measurement probabilities are with respect to measurement in the
261 computation (diagonal) basis.
262
263 Args:
264 qargs (None or list): subsystems to return probabilities for,
265 if None return for all subsystems (Default: None).
266 decimals (None or int): the number of decimal places to round
267 values. If None no rounding is done (Default: None).
268
269 Returns:
270 np.array: The Numpy vector array of probabilities.
271
272 Examples:
273
274 Consider a 2-qubit product state :math:`\\rho=\\rho_1\\otimes\\rho_0`
275 with :math:`\\rho_1=|+\\rangle\\!\\langle+|`,
276 :math:`\\rho_0=|0\\rangle\\!\\langle0|`.
277
278 .. jupyter-execute::
279
280 from qiskit.quantum_info import DensityMatrix
281
282 rho = DensityMatrix.from_label('+0')
283
284 # Probabilities for measuring both qubits
285 probs = rho.probabilities()
286 print('probs: {}'.format(probs))
287
288 # Probabilities for measuring only qubit-0
289 probs_qubit_0 = rho.probabilities([0])
290 print('Qubit-0 probs: {}'.format(probs_qubit_0))
291
292 # Probabilities for measuring only qubit-1
293 probs_qubit_1 = rho.probabilities([1])
294 print('Qubit-1 probs: {}'.format(probs_qubit_1))
295
296 We can also permute the order of qubits in the ``qargs`` list
297 to change the qubit position in the probabilities output
298
299 .. jupyter-execute::
300
301 from qiskit.quantum_info import DensityMatrix
302
303 rho = DensityMatrix.from_label('+0')
304
305 # Probabilities for measuring both qubits
306 probs = rho.probabilities([0, 1])
307 print('probs: {}'.format(probs))
308
309 # Probabilities for measuring both qubits
310 # but swapping qubits 0 and 1 in output
311 probs_swapped = rho.probabilities([1, 0])
312 print('Swapped probs: {}'.format(probs_swapped))
313 """
314 probs = self._subsystem_probabilities(
315 np.abs(self.data.diagonal()), self._dims, qargs=qargs)
316 if decimals is not None:
317 probs = probs.round(decimals=decimals)
318 return probs
319
320 def reset(self, qargs=None):
321 """Reset state or subsystems to the 0-state.
322
323 Args:
324 qargs (list or None): subsystems to reset, if None all
325 subsystems will be reset to their 0-state
326 (Default: None).
327
328 Returns:
329 DensityMatrix: the reset state.
330
331 Additional Information:
332 If all subsystems are reset this will return the ground state
333 on all subsystems. If only a some subsystems are reset this
334 function will perform evolution by the reset
335 :class:`~qiskit.quantum_info.SuperOp` of the reset subsystems.
336 """
337 if qargs is None:
338 # Resetting all qubits does not require sampling or RNG
339 state = np.zeros(2 * (self._dim, ), dtype=complex)
340 state[0, 0] = 1
341 return DensityMatrix(state, dims=self._dims)
342
343 # Reset by evolving by reset SuperOp
344 dims = self.dims(qargs)
345 reset_superop = SuperOp(ScalarOp(dims, coeff=0))
346 reset_superop.data[0] = Operator(ScalarOp(dims)).data.ravel()
347 return self.evolve(reset_superop, qargs=qargs)
348
349 @classmethod
350 def from_label(cls, label):
351 r"""Return a tensor product of Pauli X,Y,Z eigenstates.
352
353 .. list-table:: Single-qubit state labels
354 :header-rows: 1
355
356 * - Label
357 - Statevector
358 * - ``"0"``
359 - :math:`\begin{pmatrix} 1 & 0 \\ 0 & 0 \end{pmatrix}`
360 * - ``"1"``
361 - :math:`\begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix}`
362 * - ``"+"``
363 - :math:`\frac{1}{2}\begin{pmatrix} 1 & 1 \\ 1 & 1 \end{pmatrix}`
364 * - ``"-"``
365 - :math:`\frac{1}{2}\begin{pmatrix} 1 & -1 \\ -1 & 1 \end{pmatrix}`
366 * - ``"r"``
367 - :math:`\frac{1}{2}\begin{pmatrix} 1 & -i \\ i & 1 \end{pmatrix}`
368 * - ``"l"``
369 - :math:`\frac{1}{2}\begin{pmatrix} 1 & i \\ -i & 1 \end{pmatrix}`
370
371 Args:
372 label (string): a eigenstate string ket label (see table for
373 allowed values).
374
375 Returns:
376 Statevector: The N-qubit basis state density matrix.
377
378 Raises:
379 QiskitError: if the label contains invalid characters, or the length
380 of the label is larger than an explicitly specified num_qubits.
381 """
382 return DensityMatrix(Statevector.from_label(label))
383
384 @staticmethod
385 def from_int(i, dims):
386 """Return a computational basis state density matrix.
387
388 Args:
389 i (int): the basis state element.
390 dims (int or tuple or list): The subsystem dimensions of the statevector
391 (See additional information).
392
393 Returns:
394 DensityMatrix: The computational basis state :math:`|i\\rangle\\!\\langle i|`.
395
396 Additional Information:
397 The ``dims`` kwarg can be an integer or an iterable of integers.
398
399 * ``Iterable`` -- the subsystem dimensions are the values in the list
400 with the total number of subsystems given by the length of the list.
401
402 * ``Int`` -- the integer specifies the total dimension of the
403 state. If it is a power of two the state will be initialized
404 as an N-qubit state. If it is not a power of two the state
405 will have a single d-dimensional subsystem.
406 """
407 size = np.product(dims)
408 state = np.zeros((size, size), dtype=complex)
409 state[i, i] = 1.0
410 return DensityMatrix(state, dims=dims)
411
412 @classmethod
413 def from_instruction(cls, instruction):
414 """Return the output density matrix of an instruction.
415
416 The statevector is initialized in the state :math:`|{0,\\ldots,0}\\rangle` of
417 the same number of qubits as the input instruction or circuit, evolved
418 by the input instruction, and the output statevector returned.
419
420 Args:
421 instruction (qiskit.circuit.Instruction or QuantumCircuit): instruction or circuit
422
423 Returns:
424 DensityMatrix: the final density matrix.
425
426 Raises:
427 QiskitError: if the instruction contains invalid instructions for
428 density matrix simulation.
429 """
430 # Convert circuit to an instruction
431 if isinstance(instruction, QuantumCircuit):
432 instruction = instruction.to_instruction()
433 # Initialize an the statevector in the all |0> state
434 num_qubits = instruction.num_qubits
435 init = np.zeros((2**num_qubits, 2**num_qubits), dtype=complex)
436 init[0, 0] = 1
437 vec = DensityMatrix(init, dims=num_qubits * (2, ))
438 vec._append_instruction(instruction)
439 return vec
440
441 def to_dict(self, decimals=None):
442 r"""Convert the density matrix to dictionary form.
443
444 This dictionary representation uses a Ket-like notation where the
445 dictionary keys are qudit strings for the subsystem basis vectors.
446 If any subsystem has a dimension greater than 10 comma delimiters are
447 inserted between integers so that subsystems can be distinguished.
448
449 Args:
450 decimals (None or int): the number of decimal places to round
451 values. If None no rounding is done
452 (Default: None).
453
454 Returns:
455 dict: the dictionary form of the DensityMatrix.
456
457 Examples:
458
459 The ket-form of a 2-qubit density matrix
460 :math:`rho = |-\rangle\!\langle -|\otimes |0\rangle\!\langle 0|`
461
462 .. jupyter-execute::
463
464 from qiskit.quantum_info import DensityMatrix
465
466 rho = DensityMatrix.from_label('-0')
467 print(rho.to_dict())
468
469 For non-qubit subsystems the integer range can go from 0 to 9. For
470 example in a qutrit system
471
472 .. jupyter-execute::
473
474 import numpy as np
475 from qiskit.quantum_info import DensityMatrix
476
477 mat = np.zeros((9, 9))
478 mat[0, 0] = 0.25
479 mat[3, 3] = 0.25
480 mat[6, 6] = 0.25
481 mat[-1, -1] = 0.25
482 rho = DensityMatrix(mat, dims=(3, 3))
483 print(rho.to_dict())
484
485 For large subsystem dimensions delimeters are required. The
486 following example is for a 20-dimensional system consisting of
487 a qubit and 10-dimensional qudit.
488
489 .. jupyter-execute::
490
491 import numpy as np
492 from qiskit.quantum_info import DensityMatrix
493
494 mat = np.zeros((2 * 10, 2 * 10))
495 mat[0, 0] = 0.5
496 mat[-1, -1] = 0.5
497 rho = DensityMatrix(mat, dims=(2, 10))
498 print(rho.to_dict())
499 """
500 return self._matrix_to_dict(self.data,
501 self._dims,
502 decimals=decimals,
503 string_labels=True)
504
505 @property
506 def _shape(self):
507 """Return the tensor shape of the matrix operator"""
508 return 2 * tuple(reversed(self.dims()))
509
510 def _evolve_operator(self, other, qargs=None):
511 """Evolve density matrix by an operator"""
512 if qargs is None:
513 # Evolution on full matrix
514 if self._dim != other._input_dim:
515 raise QiskitError(
516 "Operator input dimension is not equal to density matrix dimension."
517 )
518 op_mat = other.data
519 mat = np.dot(op_mat, self.data).dot(op_mat.T.conj())
520 return DensityMatrix(mat, dims=other._output_dims)
521 # Otherwise we are applying an operator only to subsystems
522 # Check dimensions of subsystems match the operator
523 if self.dims(qargs) != other.input_dims():
524 raise QiskitError(
525 "Operator input dimensions are not equal to statevector subsystem dimensions."
526 )
527 # Reshape statevector and operator
528 tensor = np.reshape(self.data, self._shape)
529 # Construct list of tensor indices of statevector to be contracted
530 num_indices = len(self.dims())
531 indices = [num_indices - 1 - qubit for qubit in qargs]
532 # Left multiple by mat
533 mat = np.reshape(other.data, other._shape)
534 tensor = Operator._einsum_matmul(tensor, mat, indices)
535 # Right multiply by mat ** dagger
536 adj = other.adjoint()
537 mat_adj = np.reshape(adj.data, adj._shape)
538 tensor = Operator._einsum_matmul(tensor, mat_adj, indices, num_indices,
539 True)
540 # Replace evolved dimensions
541 new_dims = list(self.dims())
542 for i, qubit in enumerate(qargs):
543 new_dims[qubit] = other._output_dims[i]
544 new_dim = np.product(new_dims)
545 return DensityMatrix(np.reshape(tensor, (new_dim, new_dim)),
546 dims=new_dims)
547
548 def _append_instruction(self, other, qargs=None):
549 """Update the current Statevector by applying an instruction."""
550
551 # Try evolving by a matrix operator (unitary-like evolution)
552 mat = Operator._instruction_to_matrix(other)
553 if mat is not None:
554 self._data = self._evolve_operator(Operator(mat), qargs=qargs).data
555 return
556 # Otherwise try evolving by a Superoperator
557 chan = SuperOp._instruction_to_superop(other)
558 if chan is not None:
559 # Evolve current state by the superoperator
560 self._data = chan._evolve(self, qargs=qargs).data
561 return
562 # If the instruction doesn't have a matrix defined we use its
563 # circuit decomposition definition if it exists, otherwise we
564 # cannot compose this gate and raise an error.
565 if other.definition is None:
566 raise QiskitError('Cannot apply Instruction: {}'.format(
567 other.name))
568 for instr, qregs, cregs in other.definition:
569 if cregs:
570 raise QiskitError(
571 'Cannot apply instruction with classical registers: {}'.
572 format(instr.name))
573 # Get the integer position of the flat register
574 if qargs is None:
575 new_qargs = [tup.index for tup in qregs]
576 else:
577 new_qargs = [qargs[tup.index] for tup in qregs]
578 self._append_instruction(instr, qargs=new_qargs)
579
580 def _evolve_instruction(self, obj, qargs=None):
581 """Return a new statevector by applying an instruction."""
582 if isinstance(obj, QuantumCircuit):
583 obj = obj.to_instruction()
584 vec = DensityMatrix(self.data, dims=self._dims)
585 vec._append_instruction(obj, qargs=qargs)
586 return vec
587
588 def to_statevector(self, atol=None, rtol=None):
589 """Return a statevector from a pure density matrix.
590
591 Args:
592 atol (float): Absolute tolerance for checking operation validity.
593 rtol (float): Relative tolerance for checking operation validity.
594
595 Returns:
596 Statevector: The pure density matrix's corresponding statevector.
597 Corresponds to the eigenvector of the only non-zero eigenvalue.
598
599 Raises:
600 QiskitError: if the state is not pure.
601 """
602 if atol is None:
603 atol = self.atol
604 if rtol is None:
605 rtol = self.rtol
606
607 if not is_hermitian_matrix(self._data, atol=atol, rtol=rtol):
608 raise QiskitError("Not a valid density matrix (non-hermitian).")
609
610 evals, evecs = np.linalg.eig(self._data)
611
612 nonzero_evals = evals[abs(evals) > atol]
613 if len(nonzero_evals) != 1 or not np.isclose(nonzero_evals[0], 1,
614 atol=atol, rtol=rtol):
615 raise QiskitError("Density matrix is not a pure state")
616
617 psi = evecs[:, np.argmax(evals)] # eigenvectors returned in columns.
618 return Statevector(psi)
619
620 def to_counts(self):
621 """Returns the density matrix as a counts dict of probabilities.
622
623 DEPRECATED: use :meth:`probabilities_dict` instead.
624
625 Returns:
626 dict: Counts of probabilities.
627 """
628 warnings.warn(
629 'The `Statevector.to_counts` method is deprecated as of 0.13.0,'
630 ' and will be removed no earlier than 3 months after that '
631 'release date. You should use the `Statevector.probabilities_dict`'
632 ' method instead.', DeprecationWarning, stacklevel=2)
633 return self.probabilities_dict()
634
[end of qiskit/quantum_info/states/densitymatrix.py]
[start of qiskit/quantum_info/states/statevector.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017, 2019.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 """
16 Statevector quantum state class.
17 """
18
19 import re
20 import warnings
21 from numbers import Number
22
23 import numpy as np
24
25 from qiskit.circuit.quantumcircuit import QuantumCircuit
26 from qiskit.circuit.instruction import Instruction
27 from qiskit.exceptions import QiskitError
28 from qiskit.quantum_info.states.quantum_state import QuantumState
29 from qiskit.quantum_info.operators.operator import Operator
30 from qiskit.quantum_info.operators.predicates import matrix_equal
31
32
33 class Statevector(QuantumState):
34 """Statevector class"""
35
36 def __init__(self, data, dims=None):
37 """Initialize a statevector object.
38
39 Args:
40 data (vector_like): a complex statevector.
41 dims (int or tuple or list): Optional. The subsystem dimension of
42 the state (See additional information).
43
44 Raises:
45 QiskitError: if input data is not valid.
46
47 Additional Information:
48 The ``dims`` kwarg can be None, an integer, or an iterable of
49 integers.
50
51 * ``Iterable`` -- the subsystem dimensions are the values in the list
52 with the total number of subsystems given by the length of the list.
53
54 * ``Int`` or ``None`` -- the length of the input vector
55 specifies the total dimension of the density matrix. If it is a
56 power of two the state will be initialized as an N-qubit state.
57 If it is not a power of two the state will have a single
58 d-dimensional subsystem.
59 """
60 if isinstance(data, (list, np.ndarray)):
61 # Finally we check if the input is a raw vector in either a
62 # python list or numpy array format.
63 self._data = np.asarray(data, dtype=complex)
64 elif isinstance(data, Statevector):
65 self._data = data._data
66 if dims is None:
67 dims = data._dims
68 elif isinstance(data, Operator):
69 # We allow conversion of column-vector operators to Statevectors
70 input_dim, output_dim = data.dim
71 if input_dim != 1:
72 raise QiskitError("Input Operator is not a column-vector.")
73 self._data = np.ravel(data.data)
74 else:
75 raise QiskitError("Invalid input data format for Statevector")
76 # Check that the input is a numpy vector or column-vector numpy
77 # matrix. If it is a column-vector matrix reshape to a vector.
78 ndim = self._data.ndim
79 shape = self._data.shape
80 if ndim != 1:
81 if ndim == 2 and shape[1] == 1:
82 self._data = np.reshape(self._data, shape[0])
83 elif ndim != 2 or shape[1] != 1:
84 raise QiskitError("Invalid input: not a vector or column-vector.")
85 super().__init__(self._automatic_dims(dims, shape[0]))
86
87 def __eq__(self, other):
88 return super().__eq__(other) and np.allclose(
89 self._data, other._data, rtol=self.rtol, atol=self.atol)
90
91 def __repr__(self):
92 prefix = 'Statevector('
93 pad = len(prefix) * ' '
94 return '{}{},\n{}dims={})'.format(
95 prefix, np.array2string(
96 self.data, separator=', ', prefix=prefix),
97 pad, self._dims)
98
99 @property
100 def data(self):
101 """Return data."""
102 return self._data
103
104 def is_valid(self, atol=None, rtol=None):
105 """Return True if a Statevector has norm 1."""
106 if atol is None:
107 atol = self.atol
108 if rtol is None:
109 rtol = self.rtol
110 norm = np.linalg.norm(self.data)
111 return np.allclose(norm, 1, rtol=rtol, atol=atol)
112
113 def to_operator(self):
114 """Convert state to a rank-1 projector operator"""
115 mat = np.outer(self.data, np.conj(self.data))
116 return Operator(mat, input_dims=self.dims(), output_dims=self.dims())
117
118 def conjugate(self):
119 """Return the conjugate of the operator."""
120 return Statevector(np.conj(self.data), dims=self.dims())
121
122 def trace(self):
123 """Return the trace of the quantum state as a density matrix."""
124 return np.sum(np.abs(self.data) ** 2)
125
126 def purity(self):
127 """Return the purity of the quantum state."""
128 # For a valid statevector the purity is always 1, however if we simply
129 # have an arbitrary vector (not correctly normalized) then the
130 # purity is equivalent to the trace squared:
131 # P(|psi>) = Tr[|psi><psi|psi><psi|] = |<psi|psi>|^2
132 return self.trace() ** 2
133
134 def tensor(self, other):
135 """Return the tensor product state self ⊗ other.
136
137 Args:
138 other (Statevector): a quantum state object.
139
140 Returns:
141 Statevector: the tensor product operator self ⊗ other.
142
143 Raises:
144 QiskitError: if other is not a quantum state.
145 """
146 if not isinstance(other, Statevector):
147 other = Statevector(other)
148 dims = other.dims() + self.dims()
149 data = np.kron(self._data, other._data)
150 return Statevector(data, dims)
151
152 def expand(self, other):
153 """Return the tensor product state other ⊗ self.
154
155 Args:
156 other (Statevector): a quantum state object.
157
158 Returns:
159 Statevector: the tensor product state other ⊗ self.
160
161 Raises:
162 QiskitError: if other is not a quantum state.
163 """
164 if not isinstance(other, Statevector):
165 other = Statevector(other)
166 dims = self.dims() + other.dims()
167 data = np.kron(other._data, self._data)
168 return Statevector(data, dims)
169
170 def _add(self, other):
171 """Return the linear combination self + other.
172
173 Args:
174 other (Statevector): a quantum state object.
175
176 Returns:
177 Statevector: the linear combination self + other.
178
179 Raises:
180 QiskitError: if other is not a quantum state, or has
181 incompatible dimensions.
182 """
183 if not isinstance(other, Statevector):
184 other = Statevector(other)
185 if self.dim != other.dim:
186 raise QiskitError("other Statevector has different dimensions.")
187 return Statevector(self.data + other.data, self.dims())
188
189 def _multiply(self, other):
190 """Return the scalar multiplied state self * other.
191
192 Args:
193 other (complex): a complex number.
194
195 Returns:
196 Statevector: the scalar multiplied state other * self.
197
198 Raises:
199 QiskitError: if other is not a valid complex number.
200 """
201 if not isinstance(other, Number):
202 raise QiskitError("other is not a number")
203 return Statevector(other * self.data, self.dims())
204
205 def evolve(self, other, qargs=None):
206 """Evolve a quantum state by the operator.
207
208 Args:
209 other (Operator): The operator to evolve by.
210 qargs (list): a list of Statevector subsystem positions to apply
211 the operator on.
212
213 Returns:
214 Statevector: the output quantum state.
215
216 Raises:
217 QiskitError: if the operator dimension does not match the
218 specified Statevector subsystem dimensions.
219 """
220 if qargs is None:
221 qargs = getattr(other, 'qargs', None)
222
223 # Evolution by a circuit or instruction
224 if isinstance(other, (QuantumCircuit, Instruction)):
225 return self._evolve_instruction(other, qargs=qargs)
226 # Evolution by an Operator
227 if not isinstance(other, Operator):
228 other = Operator(other)
229 if qargs is None:
230 # Evolution on full statevector
231 if self._dim != other._input_dim:
232 raise QiskitError(
233 "Operator input dimension is not equal to statevector dimension."
234 )
235 return Statevector(np.dot(other.data, self.data), dims=other.output_dims())
236 # Otherwise we are applying an operator only to subsystems
237 # Check dimensions of subsystems match the operator
238 if self.dims(qargs) != other.input_dims():
239 raise QiskitError(
240 "Operator input dimensions are not equal to statevector subsystem dimensions."
241 )
242 # Reshape statevector and operator
243 tensor = np.reshape(self.data, self._shape)
244 mat = np.reshape(other.data, other._shape)
245 # Construct list of tensor indices of statevector to be contracted
246 num_indices = len(self.dims())
247 indices = [num_indices - 1 - qubit for qubit in qargs]
248 tensor = Operator._einsum_matmul(tensor, mat, indices)
249 new_dims = list(self.dims())
250 for i, qubit in enumerate(qargs):
251 new_dims[qubit] = other._output_dims[i]
252 # Replace evolved dimensions
253 return Statevector(np.reshape(tensor, np.product(new_dims)), dims=new_dims)
254
255 def equiv(self, other, rtol=None, atol=None):
256 """Return True if statevectors are equivalent up to global phase.
257
258 Args:
259 other (Statevector): a statevector object.
260 rtol (float): relative tolerance value for comparison.
261 atol (float): absolute tolerance value for comparison.
262
263 Returns:
264 bool: True if statevectors are equivalent up to global phase.
265 """
266 if not isinstance(other, Statevector):
267 try:
268 other = Statevector(other)
269 except QiskitError:
270 return False
271 if self.dim != other.dim:
272 return False
273 if atol is None:
274 atol = self.atol
275 if rtol is None:
276 rtol = self.rtol
277 return matrix_equal(self.data, other.data, ignore_phase=True,
278 rtol=rtol, atol=atol)
279
280 def probabilities(self, qargs=None, decimals=None):
281 """Return the subsystem measurement probability vector.
282
283 Measurement probabilities are with respect to measurement in the
284 computation (diagonal) basis.
285
286 Args:
287 qargs (None or list): subsystems to return probabilities for,
288 if None return for all subsystems (Default: None).
289 decimals (None or int): the number of decimal places to round
290 values. If None no rounding is done (Default: None).
291
292 Returns:
293 np.array: The Numpy vector array of probabilities.
294
295 Examples:
296
297 Consider a 2-qubit product state
298 :math:`|\\psi\\rangle=|+\\rangle\\otimes|0\\rangle`.
299
300 .. jupyter-execute::
301
302 from qiskit.quantum_info import Statevector
303
304 psi = Statevector.from_label('+0')
305
306 # Probabilities for measuring both qubits
307 probs = psi.probabilities()
308 print('probs: {}'.format(probs))
309
310 # Probabilities for measuring only qubit-0
311 probs_qubit_0 = psi.probabilities([0])
312 print('Qubit-0 probs: {}'.format(probs_qubit_0))
313
314 # Probabilities for measuring only qubit-1
315 probs_qubit_1 = psi.probabilities([1])
316 print('Qubit-1 probs: {}'.format(probs_qubit_1))
317
318 We can also permute the order of qubits in the ``qargs`` list
319 to change the qubit position in the probabilities output
320
321 .. jupyter-execute::
322
323 from qiskit.quantum_info import Statevector
324
325 psi = Statevector.from_label('+0')
326
327 # Probabilities for measuring both qubits
328 probs = psi.probabilities([0, 1])
329 print('probs: {}'.format(probs))
330
331 # Probabilities for measuring both qubits
332 # but swapping qubits 0 and 1 in output
333 probs_swapped = psi.probabilities([1, 0])
334 print('Swapped probs: {}'.format(probs_swapped))
335 """
336 probs = self._subsystem_probabilities(
337 np.abs(self.data) ** 2, self._dims, qargs=qargs)
338 if decimals is not None:
339 probs = probs.round(decimals=decimals)
340 return probs
341
342 def reset(self, qargs=None):
343 """Reset state or subsystems to the 0-state.
344
345 Args:
346 qargs (list or None): subsystems to reset, if None all
347 subsystems will be reset to their 0-state
348 (Default: None).
349
350 Returns:
351 Statevector: the reset state.
352
353 Additional Information:
354 If all subsystems are reset this will return the ground state
355 on all subsystems. If only a some subsystems are reset this
356 function will perform a measurement on those subsystems and
357 evolve the subsystems so that the collapsed post-measurement
358 states are rotated to the 0-state. The RNG seed for this
359 sampling can be set using the :meth:`seed` method.
360 """
361 if qargs is None:
362 # Resetting all qubits does not require sampling or RNG
363 state = np.zeros(self._dim, dtype=complex)
364 state[0] = 1
365 return Statevector(state, dims=self._dims)
366
367 # Sample a single measurement outcome
368 dims = self.dims(qargs)
369 probs = self.probabilities(qargs)
370 sample = self._rng.choice(len(probs), p=probs, size=1)
371
372 # Convert to projector for state update
373 proj = np.zeros(len(probs), dtype=complex)
374 proj[sample] = 1 / np.sqrt(probs[sample])
375
376 # Rotate outcome to 0
377 reset = np.eye(len(probs))
378 reset[0, 0] = 0
379 reset[sample, sample] = 0
380 reset[0, sample] = 1
381
382 # compose with reset projection
383 reset = np.dot(reset, np.diag(proj))
384 return self.evolve(
385 Operator(reset, input_dims=dims, output_dims=dims),
386 qargs=qargs)
387
388 def to_counts(self):
389 """Returns the statevector as a counts dict
390 of probabilities.
391
392 DEPRECATED: use :meth:`probabilities_dict` instead.
393
394 Returns:
395 dict: Counts of probabilities.
396 """
397 warnings.warn(
398 'The `Statevector.to_counts` method is deprecated as of 0.13.0,'
399 ' and will be removed no earlier than 3 months after that '
400 'release date. You should use the `Statevector.probabilities_dict`'
401 ' method instead.', DeprecationWarning, stacklevel=2)
402 return self.probabilities_dict()
403
404 @classmethod
405 def from_label(cls, label):
406 """Return a tensor product of Pauli X,Y,Z eigenstates.
407
408 .. list-table:: Single-qubit state labels
409 :header-rows: 1
410
411 * - Label
412 - Statevector
413 * - ``"0"``
414 - :math:`[1, 0]`
415 * - ``"1"``
416 - :math:`[0, 1]`
417 * - ``"+"``
418 - :math:`[1 / \\sqrt{2}, 1 / \\sqrt{2}]`
419 * - ``"-"``
420 - :math:`[1 / \\sqrt{2}, -1 / \\sqrt{2}]`
421 * - ``"r"``
422 - :math:`[1 / \\sqrt{2}, i / \\sqrt{2}]`
423 * - ``"l"``
424 - :math:`[1 / \\sqrt{2}, -i / \\sqrt{2}]`
425
426 Args:
427 label (string): a eigenstate string ket label (see table for
428 allowed values).
429
430 Returns:
431 Statevector: The N-qubit basis state density matrix.
432
433 Raises:
434 QiskitError: if the label contains invalid characters, or the
435 length of the label is larger than an explicitly
436 specified num_qubits.
437 """
438 # Check label is valid
439 if re.match(r'^[01rl\-+]+$', label) is None:
440 raise QiskitError('Label contains invalid characters.')
441 # We can prepare Z-eigenstates by converting the computational
442 # basis bit-string to an integer and preparing that unit vector
443 # However, for X-basis states, we will prepare a Z-eigenstate first
444 # then apply Hadamard gates to rotate 0 and 1s to + and -.
445 z_label = label
446 xy_states = False
447 if re.match('^[01]+$', label) is None:
448 # We have X or Y eigenstates so replace +,r with 0 and
449 # -,l with 1 and prepare the corresponding Z state
450 xy_states = True
451 z_label = z_label.replace('+', '0')
452 z_label = z_label.replace('r', '0')
453 z_label = z_label.replace('-', '1')
454 z_label = z_label.replace('l', '1')
455 # Initialize Z eigenstate vector
456 num_qubits = len(label)
457 data = np.zeros(1 << num_qubits, dtype=complex)
458 pos = int(z_label, 2)
459 data[pos] = 1
460 state = Statevector(data)
461 if xy_states:
462 # Apply hadamards to all qubits in X eigenstates
463 x_mat = np.array([[1, 1], [1, -1]], dtype=complex) / np.sqrt(2)
464 # Apply S.H to qubits in Y eigenstates
465 y_mat = np.dot(np.diag([1, 1j]), x_mat)
466 for qubit, char in enumerate(reversed(label)):
467 if char in ['+', '-']:
468 state = state.evolve(x_mat, qargs=[qubit])
469 elif char in ['r', 'l']:
470 state = state.evolve(y_mat, qargs=[qubit])
471 return state
472
473 @staticmethod
474 def from_int(i, dims):
475 """Return a computational basis statevector.
476
477 Args:
478 i (int): the basis state element.
479 dims (int or tuple or list): The subsystem dimensions of the statevector
480 (See additional information).
481
482 Returns:
483 Statevector: The computational basis state :math:`|i\\rangle`.
484
485 Additional Information:
486 The ``dims`` kwarg can be an integer or an iterable of integers.
487
488 * ``Iterable`` -- the subsystem dimensions are the values in the list
489 with the total number of subsystems given by the length of the list.
490
491 * ``Int`` -- the integer specifies the total dimension of the
492 state. If it is a power of two the state will be initialized
493 as an N-qubit state. If it is not a power of two the state
494 will have a single d-dimensional subsystem.
495 """
496 size = np.product(dims)
497 state = np.zeros(size, dtype=complex)
498 state[i] = 1.0
499 return Statevector(state, dims=dims)
500
501 @classmethod
502 def from_instruction(cls, instruction):
503 """Return the output statevector of an instruction.
504
505 The statevector is initialized in the state :math:`|{0,\\ldots,0}\\rangle` of the
506 same number of qubits as the input instruction or circuit, evolved
507 by the input instruction, and the output statevector returned.
508
509 Args:
510 instruction (qiskit.circuit.Instruction or QuantumCircuit): instruction or circuit
511
512 Returns:
513 Statevector: The final statevector.
514
515 Raises:
516 QiskitError: if the instruction contains invalid instructions for
517 the statevector simulation.
518 """
519 # Convert circuit to an instruction
520 if isinstance(instruction, QuantumCircuit):
521 instruction = instruction.to_instruction()
522 # Initialize an the statevector in the all |0> state
523 init = np.zeros(2 ** instruction.num_qubits, dtype=complex)
524 init[0] = 1.0
525 vec = Statevector(init, dims=instruction.num_qubits * (2,))
526 vec._append_instruction(instruction)
527 return vec
528
529 def to_dict(self, decimals=None):
530 r"""Convert the statevector to dictionary form.
531
532 This dictionary representation uses a Ket-like notation where the
533 dictionary keys are qudit strings for the subsystem basis vectors.
534 If any subsystem has a dimension greater than 10 comma delimiters are
535 inserted between integers so that subsystems can be distinguished.
536
537 Args:
538 decimals (None or int): the number of decimal places to round
539 values. If None no rounding is done
540 (Default: None).
541
542 Returns:
543 dict: the dictionary form of the Statevector.
544
545 Example:
546
547 The ket-form of a 2-qubit statevector
548 :math:`|\psi\rangle = |-\rangle\otimes |0\rangle`
549
550 .. jupyter-execute::
551
552 from qiskit.quantum_info import Statevector
553
554 psi = Statevector.from_label('-0')
555 print(psi.to_dict())
556
557 For non-qubit subsystems the integer range can go from 0 to 9. For
558 example in a qutrit system
559
560 .. jupyter-execute::
561
562 import numpy as np
563 from qiskit.quantum_info import Statevector
564
565 vec = np.zeros(9)
566 vec[0] = 1 / np.sqrt(2)
567 vec[-1] = 1 / np.sqrt(2)
568 psi = Statevector(vec, dims=(3, 3))
569 print(psi.to_dict())
570
571 For large subsystem dimensions delimeters are required. The
572 following example is for a 20-dimensional system consisting of
573 a qubit and 10-dimensional qudit.
574
575 .. jupyter-execute::
576
577 import numpy as np
578 from qiskit.quantum_info import Statevector
579
580 vec = np.zeros(2 * 10)
581 vec[0] = 1 / np.sqrt(2)
582 vec[-1] = 1 / np.sqrt(2)
583 psi = Statevector(vec, dims=(2, 10))
584 print(psi.to_dict())
585 """
586 return self._vector_to_dict(self.data,
587 self._dims,
588 decimals=decimals,
589 string_labels=True)
590
591 @property
592 def _shape(self):
593 """Return the tensor shape of the matrix operator"""
594 return tuple(reversed(self.dims()))
595
596 def _append_instruction(self, obj, qargs=None):
597 """Update the current Statevector by applying an instruction."""
598 mat = Operator._instruction_to_matrix(obj)
599 if mat is not None:
600 # Perform the composition and inplace update the current state
601 # of the operator
602 state = self.evolve(mat, qargs=qargs)
603 self._data = state.data
604 else:
605 # If the instruction doesn't have a matrix defined we use its
606 # circuit decomposition definition if it exists, otherwise we
607 # cannot compose this gate and raise an error.
608 if obj.definition is None:
609 raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
610 for instr, qregs, cregs in obj.definition:
611 if cregs:
612 raise QiskitError(
613 'Cannot apply instruction with classical registers: {}'.format(
614 instr.name))
615 # Get the integer position of the flat register
616 if qargs is None:
617 new_qargs = [tup.index for tup in qregs]
618 else:
619 new_qargs = [qargs[tup.index] for tup in qregs]
620 self._append_instruction(instr, qargs=new_qargs)
621
622 def _evolve_instruction(self, obj, qargs=None):
623 """Return a new statevector by applying an instruction."""
624 if isinstance(obj, QuantumCircuit):
625 obj = obj.to_instruction()
626 vec = Statevector(self.data, dims=self.dims())
627 vec._append_instruction(obj, qargs=qargs)
628 return vec
629
[end of qiskit/quantum_info/states/statevector.py]
[start of qiskit/transpiler/__init__.py]
1 # -*- coding: utf-8 -*-
2
3 # This code is part of Qiskit.
4 #
5 # (C) Copyright IBM 2017, 2018.
6 #
7 # This code is licensed under the Apache License, Version 2.0. You may
8 # obtain a copy of this license in the LICENSE.txt file in the root directory
9 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
10 #
11 # Any modifications or derivative works of this code must retain this
12 # copyright notice, and modified files need to carry a notice indicating
13 # that they have been altered from the originals.
14
15 """
16 =====================================
17 Transpiler (:mod:`qiskit.transpiler`)
18 =====================================
19
20 .. currentmodule:: qiskit.transpiler
21
22 Overview
23 ========
24 Transpilation is the process of rewriting a given input circuit to match
25 the topoplogy of a specific quantum device, and/or to optimize the circuit
26 for execution on present day noisy quantum systems.
27
28 Most circuits must undergo a series of transformations that make them compatible with
29 a given target device, and optimize them to reduce the effects of noise on the
30 resulting outcomes. Rewriting quantum circuits to match hardware constraints and
31 optimizing for performance can be far from trivial. The flow of logic in the rewriting
32 tool chain need not be linear, and can often have iterative sub-loops, conditional
33 branches, and other complex behaviors. That being said, the basic building blocks
34 follow the structure given below:
35
36 .. image:: /source_images/transpiling_core_steps.png
37
38 .. raw:: html
39
40 <br>
41
42 Qiskit has four pre-built transpilation pipelines available here:
43 :mod:`qiskit.transpiler.preset_passmanagers`. Unless the reader is familiar with
44 quantum circuit optimization methods and their usage, it is best to use one of
45 these ready-made routines.
46
47
48 Supplementary Information
49 =========================
50
51 .. container:: toggle
52
53 .. container:: header
54
55 **Basis Gates**
56
57 When writing a quantum circuit you are free to use any quantum gate (unitary operator) that
58 you like, along with a collection of non-gate operations such as qubit measurements and
59 reset operations. However, when running a circuit on a real quantum device one no longer
60 has this flexibility. Due to limitations in, for example, the physical interactions
61 between qubits, difficulty in implementing multi-qubit gates, control electronics etc,
62 a quantum computing device can only natively support a handful of quantum gates and non-gate
63 operations. In the present case of IBM Q devices, the native gate set can be found by querying
64 the devices themselves, and looking for the corresponding attribute in their configuration:
65
66 .. jupyter-execute::
67 :hide-code:
68 :hide-output:
69
70 from qiskit.test.mock import FakeVigo
71 backend = FakeVigo()
72
73 .. jupyter-execute::
74
75 backend.configuration().basis_gates
76
77
78 Every quantum circuit run on an IBM Q device must be expressed using only these basis gates.
79 For example, suppose one wants to run a simple phase estimation circuit:
80
81 .. jupyter-execute::
82
83 import numpy as np
84 from qiskit import QuantumCircuit
85 qc = QuantumCircuit(2, 1)
86
87 qc.h(0)
88 qc.x(1)
89 qc.cu1(np.pi/4, 0, 1)
90 qc.h(0)
91 qc.measure([0], [0])
92 qc.draw(output='mpl')
93
94 We have :math:`H`, :math:`X`, and controlled-:math:`U_{1}` gates, all of which are
95 not in our devices basis gate set, and must be expanded. This expansion is taken
96 care of for us in the :func:`qiskit.execute` function. However, we can
97 decompose the circuit to show what it would look like in the native gate set of
98 the IBM Quantum devices:
99
100 .. jupyter-execute::
101
102 qc_basis = qc.decompose()
103 qc_basis.draw(output='mpl')
104
105
106 A few things to highlight. First, the circuit has gotten longer with respect to the
107 initial one. This can be verified by checking the depth of the circuits:
108
109 .. jupyter-execute::
110
111 print('Original depth:', qc.depth(), 'Decomposed Depth:', qc_basis.depth())
112
113 Second, although we had a single controlled gate, the fact that it was not in the basis
114 set means that, when expanded, it requires more than a single `cx` gate to implement.
115 All said, unrolling to the basis set of gates leads to an increase in the depth of a
116 quantum circuit and the number of gates.
117
118 It is important to highlight two special cases:
119
120 1. A SWAP gate is not a native gate on the IBM Q devices, and must be decomposed into
121 three CNOT gates:
122
123 .. jupyter-execute::
124
125 swap_circ = QuantumCircuit(2)
126 swap_circ.swap(0, 1)
127 swap_circ.decompose().draw(output='mpl')
128
129 As a product of three CNOT gates, SWAP gates are expensive operations to perform on a
130 noisy quantum devices. However, such operations are usually necessary for embedding a
131 circuit into the limited entangling gate connectivities of actual devices. Thus,
132 minimizing the number of SWAP gates in a circuit is a primary goal in the
133 transpilation process.
134
135
136 2. A Toffoli, or controlled-controlled-not gate (`ccx`), is a three-qubit gate. Given
137 that our basis gate set includes only single- and two-qubit gates, it is obvious that
138 this gate must be decomposed. This decomposition is quite costly:
139
140 .. jupyter-execute::
141
142 ccx_circ = QuantumCircuit(3)
143 ccx_circ.ccx(0, 1, 2)
144 ccx_circ.decompose().draw(output='mpl')
145
146 For every Toffoli gate in a quantum circuit, the IBM Quantum hardware may execute up to
147 six CNOT gates, and a handful of single-qubit gates. From this example, it should be
148 clear that any algorithm that makes use of multiple Toffoli gates will end up as a
149 circuit with large depth and will therefore be appreciably affected by noise and gate
150 errors.
151
152
153 .. raw:: html
154
155 <br>
156
157 .. container:: toggle
158
159 .. container:: header
160
161 **Initial Layout**
162
163 Quantum circuits are abstract entities whose qubits are "virtual" representations of actual
164 qubits used in computations. We need to be able to map these virtual qubits in a one-to-one
165 manner to the "physical" qubits in an actual quantum device.
166
167 .. image:: /source_images/mapping.png
168
169 .. raw:: html
170
171 <br><br>
172
173 By default, qiskit will do this mapping for you. The choice of mapping depends on the
174 properties of the circuit, the particular device you are targeting, and the optimization
175 level that is chosen. The basic mapping strategies are the following:
176
177 - **Trivial layout**: Map virtual qubits to the same numbered physical qubit on the device,
178 i.e. `[0,1,2,3,4]` -> `[0,1,2,3,4]` (default in `optimization_level=0` and
179 `optimization_level=1`).
180
181 - **Dense layout**: Find the sub-graph of the device with same number of qubits as the circuit
182 with the greatest connectivity (default in `optimization_level=2` and `optimization_level=3`).
183
184
185 The choice of initial layout is extremely important when:
186
187 1. Computing the number of SWAP operations needed to map the input circuit onto the device
188 topology.
189
190 2. Taking into account the noise properties of the device.
191
192
193 The choice of `initial_layout` can mean the difference between getting a result,
194 and getting nothing but noise.
195
196 Lets see what layouts are automatically picked at various optimization levels. The modified
197 circuits returned by :func:`qiskit.compiler.transpile` have this initial layout information
198 in them, and we can view this layout selection graphically using
199 :func:`qiskit.visualization.plot_circuit_layout`:
200
201 .. jupyter-execute::
202
203 from qiskit import QuantumCircuit, transpile
204 from qiskit.visualization import plot_circuit_layout
205 from qiskit.test.mock import FakeVigo
206 backend = FakeVigo()
207
208 ghz = QuantumCircuit(3, 3)
209 ghz.h(0)
210 ghz.cx(0,range(1,3))
211 ghz.barrier()
212 ghz.measure(range(3), range(3))
213 ghz.draw(output='mpl')
214
215
216 - **Layout Using Optimization Level 0**
217
218 .. jupyter-execute::
219
220 new_circ_lv0 = transpile(ghz, backend=backend, optimization_level=0)
221 plot_circuit_layout(new_circ_lv0, backend)
222
223
224 - **Layout Using Optimization Level 3**
225
226 .. jupyter-execute::
227
228 new_circ_lv3 = transpile(ghz, backend=backend, optimization_level=3)
229 plot_circuit_layout(new_circ_lv3, backend)
230
231
232 It is completely possible to specify your own initial layout. To do so we can
233 pass a list of integers to :func:`qiskit.compiler.transpile` via the `initial_layout`
234 keyword argument, where the index labels the virtual qubit in the circuit and the
235 corresponding value is the label for the physical qubit to map onto:
236
237 .. jupyter-execute::
238
239 # Virtual -> physical
240 # 0 -> 3
241 # 1 -> 4
242 # 2 -> 2
243
244 my_ghz = transpile(ghz, backend, initial_layout=[3, 4, 2])
245 plot_circuit_layout(my_ghz, backend)
246
247 .. raw:: html
248
249 <br>
250
251
252 .. container:: toggle
253
254 .. container:: header
255
256 **Mapping Circuits to Hardware Topology**
257
258 In order to implement a CNOT gate between qubits in a quantum circuit that are not directly
259 connected on a quantum device one or more SWAP gates must be inserted into the circuit to
260 move the qubit states around until they are adjacent on the device gate map. Each SWAP
261 gate is decomposed into three CNOT gates on the IBM Quantum devices, and represents an
262 expensive and noisy operation to perform. Thus, finding the minimum number of SWAP gates
263 needed to map a circuit onto a given device, is an important step (if not the most important)
264 in the whole execution process.
265
266 However, as with many important things in life, finding the optimal SWAP mapping is hard.
267 In fact it is in a class of problems called NP-Hard, and is thus prohibitively expensive
268 to compute for all but the smallest quantum devices and input circuits. To get around this,
269 by default Qiskit uses a stochastic heuristic algorithm called
270 :class:`Qiskit.transpiler.passes.StochasticSwap` to compute a good, but not necessarily minimal
271 SWAP count. The use of a stochastic method means the circuits generated by
272 :func:`Qiskit.compiler.transpile` (or :func:`Qiskit.execute` that calls `transpile` internally)
273 are not guaranteed to be the same over repeated runs. Indeed, running the same circuit
274 repeatedly will in general result in a distribution of circuit depths and gate counts at the
275 output.
276
277 In order to highlight this, we run a GHZ circuit 100 times, using a "bad" (disconnected)
278 `initial_layout`:
279
280 .. jupyter-execute::
281
282 import matplotlib.pyplot as plt
283 from qiskit import QuantumCircuit, transpile
284 from qiskit.test.mock import FakeBoeblingen
285 backend = FakeBoeblingen()
286
287 ghz = QuantumCircuit(5)
288 ghz.h(0)
289 ghz.cx(0,range(1,5))
290 ghz.draw(output='mpl')
291
292
293 .. jupyter-execute::
294
295 depths = []
296 for _ in range(100):
297 depths.append(transpile(ghz,
298 backend,
299 initial_layout=[7, 0, 4, 15, 19],
300 ).depth())
301
302 plt.figure(figsize=(8, 6))
303 plt.hist(depths, bins=list(range(14,36)), align='left', color='#AC557C')
304 plt.xlabel('Depth', fontsize=14)
305 plt.ylabel('Counts', fontsize=14);
306
307
308 This distribution is quite wide, signaling the difficultly the SWAP mapper is having
309 in computing the best mapping. Most circuits will have a distribution of depths,
310 perhaps not as wide as this one, due to the stochastic nature of the default SWAP
311 mapper. Of course, we want the best circuit we can get, especially in cases where
312 the depth is critical to success or failure. In cases like this, it is best to
313 :func:`transpile` a circuit several times, e.g. 10, and take the one with the
314 lowest depth. The :func:`transpile` function will automatically run in parallel
315 mode, making this procedure relatively speedy in most cases.
316
317 .. raw:: html
318
319 <br>
320
321
322 .. container:: toggle
323
324 .. container:: header
325
326 **Gate Optimization**
327
328 Decomposing quantum circuits into the basis gate set of the IBM Quantum devices,
329 and the addition of SWAP gates needed to match hardware topology, conspire to
330 increase the depth and gate count of quantum circuits. Fortunately many routines
331 for optimizing circuits by combining or eliminating gates exist. In some cases
332 these methods are so effective the output circuits have lower depth than the inputs.
333 In other cases, not much can be done, and the computation may be difficult to
334 perform on noisy devices. Different gate optimizations are turned on with
335 different `optimization_level` values. Below we show the benefits gained from
336 setting the optimization level higher:
337
338 .. important::
339
340 The output from :func:`transpile` varies due to the stochastic swap mapper.
341 So the numbers below will likely change each time you run the code.
342
343
344 .. jupyter-execute::
345
346 import matplotlib.pyplot as plt
347 from qiskit import QuantumCircuit, transpile
348 from qiskit.test.mock import FakeBoeblingen
349 backend = FakeBoeblingen()
350
351 ghz = QuantumCircuit(5)
352 ghz.h(0)
353 ghz.cx(0,range(1,5))
354 ghz.draw(output='mpl')
355
356
357 .. jupyter-execute::
358
359 for kk in range(4):
360 circ = transpile(ghz, backend, optimization_level=kk)
361 print('Optimization Level {}'.format(kk))
362 print('Depth:', circ.depth())
363 print('Gate counts:', circ.count_ops())
364 print()
365
366
367 .. raw:: html
368
369 <br>
370
371
372 Transpiler API
373 ==============
374
375 Pass Manager Construction
376 -------------------------
377
378 .. autosummary::
379 :toctree: ../stubs/
380
381 PassManager
382 PassManagerConfig
383 PropertySet
384 FlowController
385
386 Layout and Topology
387 -------------------
388
389 .. autosummary::
390 :toctree: ../stubs/
391
392 Layout
393 CouplingMap
394
395 Fenced Objects
396 --------------
397
398 .. autosummary::
399 :toctree: ../stubs/
400
401 FencedDAGCircuit
402 FencedPropertySet
403
404 Exceptions
405 ----------
406
407 .. autosummary::
408 :toctree: ../stubs/
409
410 TranspilerError
411 TranspilerAccessError
412 """
413
414 from .runningpassmanager import FlowController
415 from .passmanager import PassManager
416 from .passmanager_config import PassManagerConfig
417 from .propertyset import PropertySet
418 from .exceptions import TranspilerError, TranspilerAccessError
419 from .fencedobjs import FencedDAGCircuit, FencedPropertySet
420 from .basepasses import AnalysisPass, TransformationPass
421 from .coupling import CouplingMap
422 from .layout import Layout
423
[end of qiskit/transpiler/__init__.py]
[start of qiskit/visualization/transition_visualization.py]
1 # This code is part of Qiskit.
2 #
3 # (C) Copyright IBM 2017, 2018.
4 #
5 # This code is licensed under the Apache License, Version 2.0. You may
6 # obtain a copy of this license in the LICENSE.txt file in the root directory
7 # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
8 #
9 # Any modifications or derivative works of this code must retain this
10 # copyright notice, and modified files need to carry a notice indicating
11 # that they have been altered from the originals.
12
13 """
14 Visualization function for animation of state transitions by applying gates to single qubit.
15 """
16 import sys
17 from math import sin, cos, acos, sqrt
18 import numpy as np
19
20 try:
21 import matplotlib
22 from matplotlib import pyplot as plt
23 from matplotlib import animation
24 from mpl_toolkits.mplot3d import Axes3D
25 from qiskit.visualization.bloch import Bloch
26 from qiskit.visualization.exceptions import VisualizationError
27 HAS_MATPLOTLIB = True
28 except ImportError:
29 HAS_MATPLOTLIB = False
30
31 try:
32 from IPython.display import HTML
33 HAS_IPYTHON = True
34 except ImportError:
35 HAS_IPYTHON = False
36
37
38 def _normalize(v, tolerance=0.00001):
39 """Makes sure magnitude of the vector is 1 with given tolerance"""
40
41 mag2 = sum(n * n for n in v)
42 if abs(mag2 - 1.0) > tolerance:
43 mag = sqrt(mag2)
44 v = tuple(n / mag for n in v)
45 return np.array(v)
46
47
48 class _Quaternion:
49 """For calculating vectors on unit sphere"""
50 def __init__(self):
51 self._val = None
52
53 @staticmethod
54 def from_axisangle(theta, v):
55 """Create quaternion from axis"""
56 v = _normalize(v)
57
58 new_quaternion = _Quaternion()
59 new_quaternion._axisangle_to_q(theta, v)
60 return new_quaternion
61
62 @staticmethod
63 def from_value(value):
64 """Create quaternion from vector"""
65 new_quaternion = _Quaternion()
66 new_quaternion._val = value
67 return new_quaternion
68
69 def _axisangle_to_q(self, theta, v):
70 """Convert axis and angle to quaternion"""
71 x = v[0]
72 y = v[1]
73 z = v[2]
74
75 w = cos(theta/2.)
76 x = x * sin(theta/2.)
77 y = y * sin(theta/2.)
78 z = z * sin(theta/2.)
79
80 self._val = np.array([w, x, y, z])
81
82 def __mul__(self, b):
83 """Multiplication of quaternion with quaternion or vector"""
84
85 if isinstance(b, _Quaternion):
86 return self._multiply_with_quaternion(b)
87 elif isinstance(b, (list, tuple, np.ndarray)):
88 if len(b) != 3:
89 raise Exception("Input vector has invalid length {0}".format(len(b)))
90 return self._multiply_with_vector(b)
91 else:
92 raise Exception("Multiplication with unknown type {0}".format(type(b)))
93
94 def _multiply_with_quaternion(self, q_2):
95 """Multiplication of quaternion with quaternion"""
96 w_1, x_1, y_1, z_1 = self._val
97 w_2, x_2, y_2, z_2 = q_2._val
98 w = w_1 * w_2 - x_1 * x_2 - y_1 * y_2 - z_1 * z_2
99 x = w_1 * x_2 + x_1 * w_2 + y_1 * z_2 - z_1 * y_2
100 y = w_1 * y_2 + y_1 * w_2 + z_1 * x_2 - x_1 * z_2
101 z = w_1 * z_2 + z_1 * w_2 + x_1 * y_2 - y_1 * x_2
102
103 result = _Quaternion.from_value(np.array((w, x, y, z)))
104 return result
105
106 def _multiply_with_vector(self, v):
107 """Multiplication of quaternion with vector"""
108 q_2 = _Quaternion.from_value(np.append((0.0), v))
109 return (self * q_2 * self.get_conjugate())._val[1:]
110
111 def get_conjugate(self):
112 """Conjugation of quaternion"""
113 w, x, y, z = self._val
114 result = _Quaternion.from_value(np.array((w, -x, -y, -z)))
115 return result
116
117 def __repr__(self):
118 theta, v = self.get_axisangle()
119 return "(({0}; {1}, {2}, {3}))".format(theta, v[0], v[1], v[2])
120
121 def get_axisangle(self):
122 """Returns angle and vector of quaternion"""
123 w, v = self._val[0], self._val[1:]
124 theta = acos(w) * 2.0
125
126 return theta, _normalize(v)
127
128 def tolist(self):
129 """Converts quaternion to a list"""
130 return self._val.tolist()
131
132 def vector_norm(self):
133 """Calculates norm of quaternion"""
134 _, v = self.get_axisangle()
135 return np.linalg.norm(v)
136
137
138 def visualize_transition(circuit,
139 trace=False,
140 saveas=None,
141 fpg=100,
142 spg=2):
143 """
144 Creates animation showing transitions between states of a single
145 qubit by applying quantum gates.
146
147 Args:
148 circuit (QuantumCircuit): Qiskit single-qubit QuantumCircuit. Gates supported are
149 h,x, y, z, rx, ry, rz, s, sdg, t, tdg and u1.
150 trace (bool): Controls whether to display tracing vectors - history of 10 past vectors
151 at each step of the animation.
152 saveas (str): User can choose to save the animation as a video to their filesystem.
153 This argument is a string of path with filename and extension (e.g. "movie.mp4" to
154 save the video in current working directory).
155 fpg (int): Frames per gate. Finer control over animation smoothness and computiational
156 needs to render the animation. Works well for tkinter GUI as it is, for jupyter GUI
157 it might be preferable to choose fpg between 5-30.
158 spg (int): Seconds per gate. How many seconds should animation of individual gate
159 transitions take.
160
161 Returns:
162 IPython.core.display.HTML:
163 If arg jupyter is set to True. Otherwise opens tkinter GUI and returns
164 after the GUI is closed.
165
166 Raises:
167 ImportError: Must have Matplotlib (and/or IPython) installed.
168 VisualizationError: Given gate(s) are not supported.
169
170 """
171 jupyter = False
172 if ('ipykernel' in sys.modules) and ('spyder' not in sys.modules):
173 jupyter = True
174
175 if not HAS_MATPLOTLIB:
176 raise ImportError("Must have Matplotlib installed.")
177 if not HAS_IPYTHON and jupyter is True:
178 raise ImportError("Must have IPython installed.")
179 if len(circuit.qubits) != 1:
180 raise VisualizationError("Only one qubit circuits are supported")
181
182 frames_per_gate = fpg
183 time_between_frames = (spg*1000)/fpg
184
185 # quaternions of gates which don't take parameters
186 gates = dict()
187 gates['x'] = ('x', _Quaternion.from_axisangle(np.pi / frames_per_gate, [1, 0, 0]), '#1abc9c')
188 gates['y'] = ('y', _Quaternion.from_axisangle(np.pi / frames_per_gate, [0, 1, 0]), '#2ecc71')
189 gates['z'] = ('z', _Quaternion.from_axisangle(np.pi / frames_per_gate, [0, 0, 1]), '#3498db')
190 gates['s'] = ('s', _Quaternion.from_axisangle(np.pi / 2 / frames_per_gate,
191 [0, 0, 1]), '#9b59b6')
192 gates['sdg'] = ('sdg', _Quaternion.from_axisangle(-np.pi / 2 / frames_per_gate, [0, 0, 1]),
193 '#8e44ad')
194 gates['h'] = ('h', _Quaternion.from_axisangle(np.pi / frames_per_gate, _normalize([1, 0, 1])),
195 '#34495e')
196 gates['t'] = ('t', _Quaternion.from_axisangle(np.pi / 4 / frames_per_gate, [0, 0, 1]),
197 '#e74c3c')
198 gates['tdg'] = ('tdg', _Quaternion.from_axisangle(-np.pi / 4 / frames_per_gate, [0, 0, 1]),
199 '#c0392b')
200
201 implemented_gates = ['h', 'x', 'y', 'z', 'rx', 'ry', 'rz', 's', 'sdg', 't', 'tdg', 'u1']
202 simple_gates = ['h', 'x', 'y', 'z', 's', 'sdg', 't', 'tdg']
203 list_of_circuit_gates = []
204
205 for gate in circuit._data:
206 if gate[0].name not in implemented_gates:
207 raise VisualizationError("Gate {0} is not supported".format(gate[0].name))
208 if gate[0].name in simple_gates:
209 list_of_circuit_gates.append(gates[gate[0].name])
210 else:
211 theta = gate[0].params[0]
212 rad = np.deg2rad(theta)
213 if gate[0].name == 'rx':
214 quaternion = _Quaternion.from_axisangle(rad / frames_per_gate, [1, 0, 0])
215 list_of_circuit_gates.append(('rx:'+str(theta), quaternion, '#16a085'))
216 elif gate[0].name == 'ry':
217 quaternion = _Quaternion.from_axisangle(rad / frames_per_gate, [0, 1, 0])
218 list_of_circuit_gates.append(('ry:'+str(theta), quaternion, '#27ae60'))
219 elif gate[0].name == 'rz':
220 quaternion = _Quaternion.from_axisangle(rad / frames_per_gate, [0, 0, 1])
221 list_of_circuit_gates.append(('rz:'+str(theta), quaternion, '#2980b9'))
222 elif gate[0].name == 'u1':
223 quaternion = _Quaternion.from_axisangle(rad / frames_per_gate, [0, 0, 1])
224 list_of_circuit_gates.append(('u1:'+str(theta), quaternion, '#f1c40f'))
225
226 if len(list_of_circuit_gates) == 0:
227 raise VisualizationError("Nothing to visualize.")
228
229 starting_pos = _normalize(np.array([0, 0, 1]))
230
231 fig = plt.figure(figsize=(5, 5))
232 _ax = Axes3D(fig)
233 _ax.set_xlim(-10, 10)
234 _ax.set_ylim(-10, 10)
235 sphere = Bloch(axes=_ax)
236
237 class Namespace:
238 """Helper class serving as scope container"""
239 def __init__(self):
240 self.new_vec = []
241 self.last_gate = -2
242 self.colors = []
243 self.pnts = []
244
245 namespace = Namespace()
246 namespace.new_vec = starting_pos
247
248 def animate(i):
249 sphere.clear()
250
251 # starting gate count from -1 which is the initial vector
252 gate_counter = (i-1) // frames_per_gate
253 if gate_counter != namespace.last_gate:
254 namespace.pnts.append([[], [], []])
255 namespace.colors.append(list_of_circuit_gates[gate_counter][2])
256
257 # starts with default vector [0,0,1]
258 if i == 0:
259 sphere.add_vectors(namespace.new_vec)
260 namespace.pnts[0][0].append(namespace.new_vec[0])
261 namespace.pnts[0][1].append(namespace.new_vec[1])
262 namespace.pnts[0][2].append(namespace.new_vec[2])
263 namespace.colors[0] = 'r'
264 sphere.make_sphere()
265 return _ax
266
267 namespace.new_vec = list_of_circuit_gates[gate_counter][1] * namespace.new_vec
268
269 namespace.pnts[gate_counter+1][0].append(namespace.new_vec[0])
270 namespace.pnts[gate_counter+1][1].append(namespace.new_vec[1])
271 namespace.pnts[gate_counter+1][2].append(namespace.new_vec[2])
272
273 sphere.add_vectors(namespace.new_vec)
274 if trace:
275 # sphere.add_vectors(namespace.points)
276 for point_set in namespace.pnts:
277 sphere.add_points([point_set[0], point_set[1], point_set[2]])
278
279 sphere.vector_color = [list_of_circuit_gates[gate_counter][2]]
280 sphere.point_color = namespace.colors
281 sphere.point_marker = 'o'
282
283 annotation_text = list_of_circuit_gates[gate_counter][0]
284 annotationvector = [1.4, -0.45, 1.7]
285 sphere.add_annotation(annotationvector,
286 annotation_text,
287 color=list_of_circuit_gates[gate_counter][2],
288 fontsize=30,
289 horizontalalignment='left')
290
291 sphere.make_sphere()
292
293 namespace.last_gate = gate_counter
294 return _ax
295
296 def init():
297 sphere.vector_color = ['r']
298 return _ax
299
300 ani = animation.FuncAnimation(fig,
301 animate,
302 range(frames_per_gate * len(list_of_circuit_gates)+1),
303 init_func=init,
304 blit=False,
305 repeat=False,
306 interval=time_between_frames)
307
308 if saveas:
309 ani.save(saveas, fps=30)
310 if jupyter:
311 # This is necessary to overcome matplotlib memory limit
312 matplotlib.rcParams['animation.embed_limit'] = 50
313 return HTML(ani.to_jshtml())
314 plt.show()
315 plt.close(fig)
316 return None
317
[end of qiskit/visualization/transition_visualization.py]
</code>
I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
<patch>
--- a/file.py
+++ b/file.py
@@ -1,27 +1,35 @@
def euclidean(a, b):
- while b:
- a, b = b, a % b
- return a
+ if b == 0:
+ return a
+ return euclidean(b, a % b)
def bresenham(x0, y0, x1, y1):
points = []
dx = abs(x1 - x0)
dy = abs(y1 - y0)
- sx = 1 if x0 < x1 else -1
- sy = 1 if y0 < y1 else -1
- err = dx - dy
+ x, y = x0, y0
+ sx = -1 if x0 > x1 else 1
+ sy = -1 if y0 > y1 else 1
- while True:
- points.append((x0, y0))
- if x0 == x1 and y0 == y1:
- break
- e2 = 2 * err
- if e2 > -dy:
+ if dx > dy:
+ err = dx / 2.0
+ while x != x1:
+ points.append((x, y))
err -= dy
- x0 += sx
- if e2 < dx:
- err += dx
- y0 += sy
+ if err < 0:
+ y += sy
+ err += dx
+ x += sx
+ else:
+ err = dy / 2.0
+ while y != y1:
+ points.append((x, y))
+ err -= dx
+ if err < 0:
+ x += sx
+ err += dy
+ y += sy
+ points.append((x, y))
return points
</patch>
| Qiskit/qiskit | aae6eab589915d948f89d1b131019282560df61a | `initialize` and `Statevector` don't play nicely
<!-- ⚠️ If you do not respect this template, your issue will be closed -->
<!-- ⚠️ Make sure to browse the opened and closed issues -->
### Informations
- **Qiskit Aer version**: 0.5.1
- **Python version**: 3.7.3
- **Operating system**: OSX
### What is the current behavior?
Using `initialize` in a circuit and then running with `Statevector` results in the error "Cannot apply Instruction: reset"
### Steps to reproduce the problem
```
import qiskit as qk
import qiskit.quantum_info as qi
from numpy import sqrt
n = 2
ket0 = [1/sqrt(2),0,0,1/sqrt(2)]
qc = qk.QuantumCircuit(n)
qc.initialize(ket0,range(n))
ket_qi = qi.Statevector.from_instruction(qc)
```
| Funny cause the circuit has no resets:
```
┌───┐┌───────────────┐┌───┐»
q_0: ─|0>──────────────────────────────┤ X ├┤ U3(-pi/2,0,0) ├┤ X ├»
┌──────────────┐┌───────────┐└─┬─┘└───────────────┘└─┬─┘»
q_1: ─|0>─┤ U3(pi/2,0,0) ├┤ U3(0,0,0) ├──■─────────────────────■──»
└──────────────┘└───────────┘ »
« ┌──────────────┐┌───┐┌───────────┐┌───┐┌───────────┐
«q_0: ┤ U3(pi/2,0,0) ├┤ X ├┤ U3(0,0,0) ├┤ X ├┤ U3(0,0,0) ├
« └──────────────┘└─┬─┘└───────────┘└─┬─┘└───────────┘
«q_1: ──────────────────■─────────────────■───────────────
«
```
That circuit has a lot of things I don't like, but at least it has no resets.
@nonhermitian That circuit *does* have resets, notice the two |0> |0> at the start? The definition of initialize has reset instructions on all qubits that are removed by the transpiler if it is the first instruction in a circuit. This is so you can "initialize" subsets of qubits at any stage of a circuit.
I'm transferring this to Qiskit Terra. In the future please post issues related to the Quantum info module in Terra, not Aer.
That is not just the initial state of the qubits? That is what used to be there. It is hard to tell the difference here. So that means that initialize is no longer a unitary operation like it was before? I guess I missed that as well.
A reset method was added to the Statevector class last release, so the simulation could be updated to work with initialize via adding support of reset to the the from_instruction method.
@nonhermitian I think this was changed recently in the draw function. Now circuits are drawn without any initial states, so any time you see them they refer to reset instructions.
Yeah, it is just that the original text drawing is very close to the reset instructions so it is hard for the user to understand. | 2020-05-15T16:51:32Z | <patch>
diff --git a/qiskit/quantum_info/states/densitymatrix.py b/qiskit/quantum_info/states/densitymatrix.py
--- a/qiskit/quantum_info/states/densitymatrix.py
+++ b/qiskit/quantum_info/states/densitymatrix.py
@@ -547,12 +547,22 @@ def _evolve_operator(self, other, qargs=None):
def _append_instruction(self, other, qargs=None):
"""Update the current Statevector by applying an instruction."""
+ from qiskit.circuit.reset import Reset
+ from qiskit.circuit.barrier import Barrier
# Try evolving by a matrix operator (unitary-like evolution)
mat = Operator._instruction_to_matrix(other)
if mat is not None:
self._data = self._evolve_operator(Operator(mat), qargs=qargs).data
return
+
+ # Special instruction types
+ if isinstance(other, Reset):
+ self._data = self.reset(qargs)._data
+ return
+ if isinstance(other, Barrier):
+ return
+
# Otherwise try evolving by a Superoperator
chan = SuperOp._instruction_to_superop(other)
if chan is not None:
diff --git a/qiskit/quantum_info/states/statevector.py b/qiskit/quantum_info/states/statevector.py
--- a/qiskit/quantum_info/states/statevector.py
+++ b/qiskit/quantum_info/states/statevector.py
@@ -595,29 +595,40 @@ def _shape(self):
def _append_instruction(self, obj, qargs=None):
"""Update the current Statevector by applying an instruction."""
+ from qiskit.circuit.reset import Reset
+ from qiskit.circuit.barrier import Barrier
+
mat = Operator._instruction_to_matrix(obj)
if mat is not None:
# Perform the composition and inplace update the current state
# of the operator
- state = self.evolve(mat, qargs=qargs)
- self._data = state.data
- else:
- # If the instruction doesn't have a matrix defined we use its
- # circuit decomposition definition if it exists, otherwise we
- # cannot compose this gate and raise an error.
- if obj.definition is None:
- raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
- for instr, qregs, cregs in obj.definition:
- if cregs:
- raise QiskitError(
- 'Cannot apply instruction with classical registers: {}'.format(
- instr.name))
- # Get the integer position of the flat register
- if qargs is None:
- new_qargs = [tup.index for tup in qregs]
- else:
- new_qargs = [qargs[tup.index] for tup in qregs]
- self._append_instruction(instr, qargs=new_qargs)
+ self._data = self.evolve(mat, qargs=qargs).data
+ return
+
+ # Special instruction types
+ if isinstance(obj, Reset):
+ self._data = self.reset(qargs)._data
+ return
+ if isinstance(obj, Barrier):
+ return
+
+ # If the instruction doesn't have a matrix defined we use its
+ # circuit decomposition definition if it exists, otherwise we
+ # cannot compose this gate and raise an error.
+ if obj.definition is None:
+ raise QiskitError('Cannot apply Instruction: {}'.format(obj.name))
+
+ for instr, qregs, cregs in obj.definition:
+ if cregs:
+ raise QiskitError(
+ 'Cannot apply instruction with classical registers: {}'.format(
+ instr.name))
+ # Get the integer position of the flat register
+ if qargs is None:
+ new_qargs = [tup.index for tup in qregs]
+ else:
+ new_qargs = [qargs[tup.index] for tup in qregs]
+ self._append_instruction(instr, qargs=new_qargs)
def _evolve_instruction(self, obj, qargs=None):
"""Return a new statevector by applying an instruction."""
</patch> | [] | [] | |||
ipython__ipython-10213 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | ipython/ipython | 78ec96d7ca0147f0655d5260f2ab0c61d94e4279 | "remove usage of backports.shutil_get_terminal_size\nThis is for pre-3.3 Python.\r\n\r\nPretty easy (...TRUNCATED) | 2017-01-28T05:22:06Z | "<patch>\ndiff --git a/IPython/utils/terminal.py b/IPython/utils/terminal.py\n--- a/IPython/utils/te(...TRUNCATED) | [] | [] | ||||
Qiskit__qiskit-1295 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | Qiskit/qiskit | 77dc51b93e7312bbff8f5acf7d8242232bd6624f | "credentials failed for qiskit ver 0.6.1\n<!-- ⚠️ If you do not respect this template, your issu(...TRUNCATED) | "Can you try enable_account or regenerating the token. Your code should work. If you type `IBMQ.stor(...TRUNCATED) | 2018-11-19T08:27:15Z | "<patch>\ndiff --git a/qiskit/backends/ibmq/credentials/_configrc.py b/qiskit/backends/ibmq/credenti(...TRUNCATED) | [] | [] | |||
docker__compose-6410 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | docker/compose | 14e7a11b3c96f0ea818c8c28d84a1aff7967e579 | "Upgrade `events` to use the new API fields\nIn API version 1.22 the events structure was updated to(...TRUNCATED) | "This upgrade may also allow us to handle image events. Since we won't need to inspect every event, (...TRUNCATED) | 2018-12-11T01:55:02Z | "<patch>\ndiff --git a/compose/cli/log_printer.py b/compose/cli/log_printer.py\n--- a/compose/cli/lo(...TRUNCATED) | [] | [] | |||
ytdl-org__youtube-dl-1591 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | ytdl-org/youtube-dl | b4cdc245cf0af0672207a5090cb6eb6c29606cdb | "Opus audio conversion failure\nWhen trying to extract and convert audio from a youtube video into a(...TRUNCATED) | "I had the same issue and after an hour-long debug session with a friend of mine we found out that t(...TRUNCATED) | 2013-10-12T11:32:27Z | "<patch>\ndiff --git a/youtube_dl/PostProcessor.py b/youtube_dl/PostProcessor.py\n--- a/youtube_dl/P(...TRUNCATED) | [] | [] | |||
numpy__numpy-13703 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | numpy/numpy | 40ada70d9efc903097b2ff3f968c23a7e2f14296 | "Dtype.base attribute not documented\ndtype instances have a `base` attribute that, I think, is mean(...TRUNCATED) | "I've found myself using `.subdtype` instead, which contains the same info\nI think base also means (...TRUNCATED) | 2019-06-03T20:15:13Z | "<patch>\ndiff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py\n--- a/numpy/core/_ad(...TRUNCATED) | [] | [] | |||
wagtail__wagtail-7855 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | wagtail/wagtail | 4550bba562286992b27e667b071451e6886fbf44 | "Use html_url in alias_of API serialisation\nAs per https://github.com/wagtail/wagtail/pull/7669#iss(...TRUNCATED) | 2022-01-13T15:50:23Z | "<patch>\ndiff --git a/wagtail/api/v2/serializers.py b/wagtail/api/v2/serializers.py\n--- a/wagtail/(...TRUNCATED) | [] | [] | ||||
pandas-dev__pandas-27237 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | pandas-dev/pandas | f683473a156f032a64a1d7edcebde21c42a8702d | "Add key to sorting functions\nMany python functions (sorting, max/min) accept a key argument, perh(...TRUNCATED) | "Here's a specific use case that came up on [StackOverflow](http://stackoverflow.com/questions/29580(...TRUNCATED) | 2019-07-04T23:04:26Z | "<patch>\ndiff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst\n--- a/do(...TRUNCATED) | [] | [] | |||
conan-io__conan-3187 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | conan-io/conan | c3baafb780b6e5498f8bd460426901d9d5ab10e1 | "Using \"?\" in a tools.get() URL will fail, while using it in a tools.download() will succeed.\nTo (...TRUNCATED) | "Verified: ``tools.get()`` uses the last part of the URL as the name of the file to be saved.\r\n\r\(...TRUNCATED) | 2018-07-10T10:30:23Z | "<patch>\ndiff --git a/conans/client/tools/net.py b/conans/client/tools/net.py\n--- a/conans/client/(...TRUNCATED) | [] | [] | |||
googleapis__google-cloud-python-3348 | "You will be provided with a partial code base and an issue statement explaining a problem to resolv(...TRUNCATED) | googleapis/google-cloud-python | 520637c245d461db8ee45ba466d763036b82ea42 | Error reporting system tests needed
Follow up to #3263.
| 2017-05-01T22:40:28Z | "<patch>\ndiff --git a/error_reporting/nox.py b/error_reporting/nox.py\n--- a/error_reporting/nox.py(...TRUNCATED) | [] | [] |
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Dataset Card for "SWE-bench_bm25_40K"
Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
This dataset SWE-bench_bm25_40K includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 40,000 cl100k_base tokens from the tiktoken tokenization package used for OpenAI models.
The text column can be used directly with LMs to generate patch files.
Models are instructed to generate patch formatted file using the following template:
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
This format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.
Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
Dataset Structure
Data Instances
An example of a SWE-bench datum is as follows:
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
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