Repository: Infleqtion/client-superstaq (language: python)

Issue:
Feature/base qcvv framework
In preparation for the XEB/IRB benchmarking routines, this PR implements some common functionality that can form the basis of a wide QCVV within superstaq.


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Fix patch:
diff --git a/general-superstaq/requirements.txt b/general-superstaq/requirements.txt
index 4fba6a917..b982f35d4 100644
--- a/general-superstaq/requirements.txt
+++ b/general-superstaq/requirements.txt
@@ -1,3 +1,4 @@
 numpy>=1.21.0
 pydantic>=1.10.7
 requests>=2.30.0
+seaborn>=0.13.2
\ No newline at end of file
diff --git a/supermarq-benchmarks/examples/qcvv/qcvv.rst b/supermarq-benchmarks/examples/qcvv/qcvv.rst
index 9648b27c1..be220eb31 100644
--- a/supermarq-benchmarks/examples/qcvv/qcvv.rst
+++ b/supermarq-benchmarks/examples/qcvv/qcvv.rst
@@ -10,3 +10,15 @@ For a demonstration of how to implement a new experiment take a look at the foll
     :maxdepth: 1
 
     qcvv_css
+
+
+Alternatively for pre-build experiments that can be used out of the box see
+
+.. toctree::
+    :maxdepth: 1
+
+    qcvv_xeb_css
+
+.. note::
+
+    At present the QCVV library is only available in :code:`cirq-superstaq`.
\ No newline at end of file
diff --git a/supermarq-benchmarks/examples/qcvv/qcvv_xeb_css.ipynb b/supermarq-benchmarks/examples/qcvv/qcvv_xeb_css.ipynb
new file mode 100644
index 000000000..357525bcd
--- /dev/null
+++ b/supermarq-benchmarks/examples/qcvv/qcvv_xeb_css.ipynb
@@ -0,0 +1,349 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cross Entropy Benchmarking (XEB)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To demonstrate the cross entropy benchmarking routine, consider a noise model with two independent\n",
+    "depolarising channels, one which only effects single qubit gates and a second that only effects\n",
+    "two qubit gates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import annotations\n",
+    "import cirq\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "class IndependentDepolariseNoiseModel(cirq.NoiseModel):\n",
+    "    \"\"\"Applies single and two qubit depolarising channels independently\"\"\"\n",
+    "\n",
+    "    def __init__(self, single_qubit_error: float, two_qubit_error: float) -> None:\n",
+    "        \"\"\"Args:\n",
+    "        single_qubit_error: Single qubit pauli error\n",
+    "        two_qubit_error: Two qubit pauli error\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        self.single_qubit_error = single_qubit_error\n",
+    "        self.two_qubit_error = two_qubit_error\n",
+    "\n",
+    "        self.single_qubit_depolarise = cirq.DepolarizingChannel(p=single_qubit_error, n_qubits=1)\n",
+    "        self.two_qubit_depolarise = cirq.DepolarizingChannel(p=two_qubit_error, n_qubits=2)\n",
+    "\n",
+    "    def noisy_operation(self, operation: cirq.Operation) -> list[cirq.OP_TREE]:\n",
+    "        \"\"\"Produces a list of operations by applying each noise model\n",
+    "        to the provided operation depending on the number of qubits it acts on.\n",
+    "        \"\"\"\n",
+    "        if len(operation.qubits) == 1:\n",
+    "            return [operation, self.single_qubit_depolarise(*operation.qubits)]\n",
+    "\n",
+    "        if len(operation.qubits) == 2:\n",
+    "            return [operation, self.two_qubit_depolarise(*operation.qubits)]\n",
+    "\n",
+    "        return [operation]\n",
+    "\n",
+    "\n",
+    "noise = IndependentDepolariseNoiseModel(single_qubit_error=0.005, two_qubit_error=0.02)\n",
+    "simulator = cirq.DensityMatrixSimulator(\n",
+    "    noise=noise,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The XEB experiment allows us to estimate the fidelity of a \"cycle\" composed of pair of single \n",
+    "qubit gates followed by a two qubit gate. If each single qubit gate is effected by a pauli error\n",
+    "channel with rate $p_1$ and each two qubit gate is effected by a two qubit \n",
+    "depolarising channel with pauli $p_2$ the overall cycle depolarising error is\n",
+    "$$e_L = \\frac{16}{15}\\left( 1 - \\left(1- p_1\\right)^2 \\left(1-p_2\\right) \\right)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9a5cc055a4543269844a578018d51f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Building circuits:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "457586904daa46e3a4f0e8e8a503cafc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Simulating circuits:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from supermarq.qcvv import XEB\n",
+    "\n",
+    "\n",
+    "experiment = XEB()\n",
+    "experiment.prepare_experiment(num_circuits=25, cycle_depths=[1, 10, 50, 100])\n",
+    "experiment.run_with_simulator(simulator=simulator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed7dcc38897d4009a5cdcb2642576bf3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Evaluating circuits:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cycle depolarising error: 0.03176\n",
+      "Estimated cycle depolarising error: 0.03175\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 594.486x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 594.486x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if experiment.collect_data():\n",
+    "    experiment.analyze_results(plot_results=True)\n",
+    "cycle_depolarising_error = (\n",
+    "    16 / 15 * (1 - (1 - noise.single_qubit_error) ** 2 * (1 - noise.two_qubit_error))\n",
+    ")\n",
+    "cycle_depolarising_error_estimate = 1 - experiment.results.cycle_fidelity_estimate\n",
+    "print(f\"Cycle depolarising error: {cycle_depolarising_error:.5f}\")\n",
+    "print(f\"Estimated cycle depolarising error: {cycle_depolarising_error_estimate:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now lets repeat a similar experiment, still using two qubits but this time without entangling \n",
+    "gates between them. In this case our noise model reduces to a pair of independent single \n",
+    "qubit depolarising channels with pauli rates $p_1$, or equivalently depolarising rates of \n",
+    "$e_{1q} = \\tfrac34 p_1$. \n",
+    "\n",
+    "Each cycle is made up of two single qubit gates (one on each qubit) so we obtain a cycle fidelity of\n",
+    "$$f_L = (1- e_{1q})^2$$\n",
+    "\n",
+    "Thus we can estimate the single qubit pauli error as \n",
+    "$$p_1 = \\frac{4}{3} \\left(1-\\sqrt{f_L}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c08d7da06acb478cbee474414798a31e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Building circuits:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6aa6347887848cf9c501d3d6e5b87e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Simulating circuits:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "single_qubit_experiment = XEB(two_qubit_gate=None)\n",
+    "single_qubit_experiment.prepare_experiment(num_circuits=25, cycle_depths=[10, 50, 100, 200])\n",
+    "single_qubit_experiment.run_with_simulator(simulator=simulator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24a15eaf54354b60afa39961b047cf4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Evaluating circuits:   0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Single qubit pauli error: 0.00500\n",
+      "Estimated single qubit pauli error: 0.00532\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 594.486x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 594.486x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if single_qubit_experiment.collect_data():\n",
+    "    single_qubit_experiment.analyze_results(plot_results=True)\n",
+    "single_qubit_pauli_error_estimate = (\n",
+    "    4 / 3 * (1 - np.sqrt(single_qubit_experiment.results.cycle_fidelity_estimate))\n",
+    ")\n",
+    "\n",
+    "print(f\"Single qubit pauli error: {noise.single_qubit_error:.5f}\")\n",
+    "print(f\"Estimated single qubit pauli error: {single_qubit_pauli_error_estimate:.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now rearrange the equation above to estimate the two qubit depolarising error\n",
+    "$$ e_{2q}  = \\frac{16}{15} \\left(1 - \\frac{1-\\tfrac{15}{16}e_L}{\\left(1 - \\tfrac43 e_{1q}\\right)^2} \\right)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Two qubit depolarising error: 0.02133\n",
+      "Estimated two qubit depolarising error: 0.02065\n"
+     ]
+    }
+   ],
+   "source": [
+    "two_qubit_depolarising_error = 16 / 15 * noise.two_qubit_error\n",
+    "two_qubit_depolarising_error_estimate = (16 / 15) * (\n",
+    "    1\n",
+    "    - (\n",
+    "        (1 - 15 / 16 * cycle_depolarising_error_estimate)\n",
+    "        / (1 - single_qubit_pauli_error_estimate) ** 2\n",
+    "    )\n",
+    ")\n",
+    "print(f\"Two qubit depolarising error: {two_qubit_depolarising_error:.5f}\")\n",
+    "print(f\"Estimated two qubit depolarising error: {two_qubit_depolarising_error_estimate:.5f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "client_superstaq",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/supermarq-benchmarks/supermarq/qcvv/__init__.py b/supermarq-benchmarks/supermarq/qcvv/__init__.py
index 895631c56..14297c050 100644
--- a/supermarq-benchmarks/supermarq/qcvv/__init__.py
+++ b/supermarq-benchmarks/supermarq/qcvv/__init__.py
@@ -1,5 +1,13 @@
 """A toolkit of QCVV routines."""
 
 from .base_experiment import BenchmarkingExperiment, BenchmarkingResults, Sample
+from .xeb import XEB, XEBResults, XEBSample
 
-__all__ = ["BenchmarkingExperiment", "BenchmarkingResults", "Sample"]
+__all__ = [
+    "BenchmarkingExperiment",
+    "BenchmarkingResults",
+    "Sample",
+    "XEB",
+    "XEBResults",
+    "XEBSample",
+]
diff --git a/supermarq-benchmarks/supermarq/qcvv/xeb.py b/supermarq-benchmarks/supermarq/qcvv/xeb.py
new file mode 100644
index 000000000..f3c2a5ddb
--- /dev/null
+++ b/supermarq-benchmarks/supermarq/qcvv/xeb.py
@@ -0,0 +1,392 @@
+# Copyright 2021 The Cirq Developers
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tooling for cross entropy benchmark experiments.
+"""
+from __future__ import annotations
+
+import itertools
+import random
+from collections.abc import Iterable, Sequence
+from dataclasses import dataclass, field
+
+import cirq
+import numpy as np
+import pandas as pd
+import scipy
+import seaborn as sns
+import tqdm
+import tqdm.contrib
+import tqdm.contrib.itertools
+
+from supermarq.qcvv import BenchmarkingExperiment, BenchmarkingResults, Sample
+
+
+@dataclass
+class XEBSample(Sample):
+    """The samples used in XEB experiments."""
+
+    target_probabilities: dict[str, float] = field(init=False)
+    """The target probabilities obtained through a noiseless simulator"""
+    sample_probabilities: dict[str, float] = field(init=False)
+    """The sample probabilities obtained from the chosen target"""
+
+    def sum_target_probs_square(self) -> float:
+        """Compute the sum of the squared target probabilities.
+
+        Raises:
+            RuntimeError: If no target probabilities have been initialised.
+
+        Returns:
+            float: The sum of squared target probabilities.
+        """
+        if not hasattr(self, "target_probabilities"):
+            raise RuntimeError("`target_probabilities` have not yet been initialised")
+
+        return sum(prob**2 for prob in self.target_probabilities.values())
+
+    def sum_target_cross_sample_probs(self) -> float:
+        """Compute the dot product between the sample and target probabilities
+
+        Raises:
+            RuntimeError: If either the target or sample probabilities have not yet been
+                initialised.
+
+        Returns:
+            float: The dot product between the sample and target probabilities.
+        """
+        if not hasattr(self, "target_probabilities"):
+            raise RuntimeError("`target_probabilities` have not yet been initialised")
+
+        if not hasattr(self, "sample_probabilities"):
+            raise RuntimeError("`sample_probabilities` have not yet been initialised")
+
+        return sum(
+            self.target_probabilities[state] * self.sample_probabilities[state]
+            for state in self.target_probabilities
+        )
+
+
+@dataclass(frozen=True)
+class XEBResults(BenchmarkingResults):
+    """Results from an XEB experiment."""
+
+    cycle_fidelity_estimate: float
+    """Estimated cycle fidelity."""
+    cycle_fidelity_estimate_std: float
+    """Standard deviation for the cycle fidelity estimate."""
+
+    experiment_name = "IRB"
+
+
+class XEB(BenchmarkingExperiment[XEBResults]):
+    r"""Cross-entropy benchmarking (XEB) experiment.
+
+    The XEB experiment can be used to estimate the combined fidelity of a repeating
+    cycle of gates. In our case, where we restrict ourselves to two qubits, we use
+    cycles made up of two randomly selected single qubit phased XZ gates and a constant
+    two qubit gate. This is illustrated as follows:
+
+    .. image:: ../../superstaq/qcvv/XEB_Random_Circuit.png
+
+    For each randomly generated circuit, with a given number of cycle, we compare the
+    simulated state probabilities, :math:`p(x)` with those achieved by running the circuit
+    on a given target, :math:`\hat{p}(x)`. The fidelity of a circuit containing :math:`d`
+    cycles, :math:`f_d` can then be estimated as
+
+    .. math::
+
+        \sum_{x \in \{0, 1\}^n} p(x) \hat{p}(x) - \frac{1}{2^n} =
+        f_d \left(\sum_{x \in \{0, 1\}^n} p(x)^2 -  \frac{1}{2^n}\right)
+
+    We can therefore fit a linear model to estimate the value of :math:`f_d`. We the estimate
+    the fidelity of the cycle, :math:`f_{\mathrm{cycle}}` as
+
+    .. math::
+
+        f_d = A(f_{cycle})^d
+
+    Thus fitting another linear model to :math:`\log(f_d) \sim d` provides us with an estimate
+    of the cycle fidelity.
+    """
+
+    def __init__(
+        self,
+        single_qubit_gate_set: list[cirq.Gate] | None = None,
+        two_qubit_gate: cirq.Gate | None = cirq.CZ,
+    ) -> None:
+        """Args:
+        single_qubit_gate_set: Optional list of single qubit gates to randomly sample
+            from when generating random circuits. If not provided defaults to phased
+            XZ gates with 1/4 pi intervals.
+        two_qubit_gate: The two qubit gate to interleave between the single qubit gates. If
+            None then no two qubit gate is used. Defaults to control-Z gate.
+        """
+        super().__init__(num_qubits=2)
+
+        self._circuit_fidelities: pd.DataFrame | None = None
+
+        self._samples: Sequence[XEBSample] | None = None  # Overwrite with modified sampled object
+
+        self.two_qubit_gate: cirq.Gate | None = two_qubit_gate
+        """The two qubit gate to use for interleaving."""
+
+        self.single_qubit_gate_set: list[cirq.Gate]
+        """The single qubit gates to randomly sample from"""
+
+        if single_qubit_gate_set is None:
+            gate_exponents = np.linspace(start=0, stop=7 / 4, num=8)
+            self.single_qubit_gate_set = [
+                cirq.PhasedXZGate(
+                    z_exponent=z,  # 1) Choose an axis in the xy-plane, zπ from the +x-axis.
+                    x_exponent=0.5,  # 2) Rotate about the axis in 1) by a fixed π/2.
+                    axis_phase_exponent=a,  # 3) Rotate about the +z-axis by aπ (a final phasing).
+                )
+                for a, z in itertools.product(
+                    gate_exponents, repeat=2
+                )  # enumerates every possible (a, z)
+            ]
+        else:
+            self.single_qubit_gate_set = single_qubit_gate_set
+
+    ##############
+    # Properties #
+    ##############
+    @property
+    def circuit_fidelities(self) -> pd.DataFrame:
+        """The circuit fidelity calculations from the most recently run experiment.
+
+        Raises:
+            RuntimeError: If no data is available.
+        """
+        if self._circuit_fidelities is None:
+            raise RuntimeError("No data to retrieve. The experiment has not been run.")
+
+        return self._circuit_fidelities
+
+    @property
+    def samples(self) -> Sequence[XEBSample]:  # Overwrite with XEBSample return type
+        """The samples generated during the experiment.
+
+        Raises:
+            RuntimeError: If no samples are available.
+        """
+        if self._samples is None:
+            raise RuntimeError("No samples to retrieve. The experiment has not been run.")
+
+        return self._samples
+
+    ###################
+    # Private Methods #
+    ###################
+    def _build_circuits(
+        self,
+        num_circuits: int,
+        cycle_depths: Iterable[int],
+    ) -> Sequence[XEBSample]:
+        """Build a list of random circuits to perform the XEB experiment with.
+
+        Args:
+            num_circuits: Number of circuits to generate.
+            cycle_depths: An iterable of the different numbers of cycles to include in each circuit.
+
+        Returns:
+            The list of experiment samples.
+        """
+        random_circuits = []
+        for _, depth in tqdm.contrib.itertools.product(
+            range(num_circuits), cycle_depths, desc="Building circuits"
+        ):
+            circuit = cirq.Circuit()
+            for _ in range(depth + int(self.two_qubit_gate is not None)):
+                circuit.append(
+                    [
+                        gate(qubit)
+                        for gate, qubit in zip(
+                            random.choices(self.single_qubit_gate_set, k=2), self.qubits
+                        )
+                    ]
+                )
+
+            if self.two_qubit_gate is not None:
+                circuit = self._interleave_op(circuit, self.two_qubit_gate(*self.qubits))
+
+            random_circuits.append(
+                XEBSample(
+                    raw_circuit=circuit + cirq.measure(sorted(circuit.all_qubits())),
+                    data={
+                        "circuit_depth": len(circuit),
+                        "num_cycles": depth,
+                        "two_qubit_gate": str(self.two_qubit_gate),
+                    },
+                )
+            )
+
+        return random_circuits
+
+    def _process_probabilities(
+        self, samples: Sequence[XEBSample]  # type: ignore[override]
+    ) -> pd.DataFrame:
+        """Processes the probabilities generated by sampling the circuits into the data structures
+        needed for analyzing the results.
+
+        Args:
+            samples: The list of samples to process the results from.
+
+        Returns:
+            A data frame of the full results needed to analyse the experiment.
+        """
+
+        for sample in samples:
+            sample.sample_probabilities = sample.probabilities
+            sample.probabilities = {}
+
+        for sample in tqdm.notebook.tqdm(samples, desc="Evaluating circuits"):
+            sample.target_probabilities = self._simulate_sample(sample)
+
+        records = []
+        for sample in samples:
+            target_probabilities = {
+                f"p({key})": value for key, value in sample.target_probabilities.items()
+            }
+            sample_probabilities = {
+                f"p^({key})": value for key, value in sample.sample_probabilities.items()
+            }
+            records.append(
+                {
+                    "cycle_depth": sample.data["num_cycles"],
+                    "circuit_depth": sample.data["circuit_depth"],
+                    **target_probabilities,
+                    **sample_probabilities,
+                    "sum_p(x)p(x)": sample.sum_target_probs_square(),
+                    "sum_p(x)p^(x)": sample.sum_target_cross_sample_probs(),
+                }
+            )
+        return pd.DataFrame(records)
+
+    def _simulate_sample(self, sample: XEBSample) -> dict[str, float]:
+        """Simulates the exact probabilities of measuring all possible bitstrings
+        with a given sample.
+
+        Args:
+            sample: The sample to simulate.
+
+        Returns:
+            A dictionary of the probability of each bitstring.
+        """
+        sim = cirq.Simulator()
+
+        result = sim.simulate(
+            cirq.drop_terminal_measurements(sample.circuit),
+            qubit_order=sorted(sample.circuit.all_qubits()),
+        )
+        return {
+            f"{i:0{self.num_qubits}b}": np.abs(amp) ** 2
+            for i, amp in enumerate(result.final_state_vector)
+        }
+
+    ###################
+    # Public Methods  #
+    ###################
+    def analyze_results(self, plot_results: bool = True) -> XEBResults:
+        """Analyse the results and calculate the estimated circuit fidelity.
+
+        Args:
+            plot_results (optional): Whether to generate the data plots. Defaults to True.
+
+        Returns:
+           The final results from the experiment.
+        """
+
+        # Fit a linear model for each cycle depth to estimate the circuit fidelity
+        records = []
+        for depth in set(self.raw_data.cycle_depth):
+            df = self.raw_data[self.raw_data.cycle_depth == depth]
+            fit = scipy.stats.linregress(
+                x=df["sum_p(x)p(x)"] - 1 / 2**self.num_qubits,
+                y=df["sum_p(x)p^(x)"] - 1 / 2**self.num_qubits,
+            )
+
+            records.append(
+                {
+                    "cycle_depth": depth,
+                    "circuit_fidelity_estimate": fit.slope,
+                    "circuit_fidelity_estimate_std": fit.stderr,
+                }
+            )
+
+        self._circuit_fidelities = pd.DataFrame(records)
+
+        self.circuit_fidelities["log_fidelity_estimate"] = np.log(
+            self.circuit_fidelities.circuit_fidelity_estimate
+        )
+
+        # Fit a linear model to the depth ~ log(fidelity) to approximate the cycle fidelity
+        cycle_fit = scipy.stats.linregress(
+            x=self.circuit_fidelities.cycle_depth,
+            y=np.log(self.circuit_fidelities.circuit_fidelity_estimate),
+        )
+        cycle_fidelity_estimate = np.exp(cycle_fit.slope)
+        cycle_fidelity_estimate_std = cycle_fidelity_estimate * cycle_fit.stderr
+
+        self._results = XEBResults(
+            target="$ ".join(self.targets),
+            total_circuits=len(self.samples),
+            cycle_fidelity_estimate=cycle_fidelity_estimate,
+            cycle_fidelity_estimate_std=cycle_fidelity_estimate_std,
+        )
+
+        if plot_results:
+            self.plot_results()
+
+        return self.results
+
+    def plot_results(self) -> None:
+        """Plot the experiment data and the corresponding fits."""
+        plot_1 = sns.lmplot(
+            data=self.raw_data,
+            x="sum_p(x)p(x)",
+            y="sum_p(x)p^(x)",
+            hue="cycle_depth",
+            palette="dark:r",
+            legend="full",
+            ci=None,
+        )
+        sns.move_legend(plot_1, "center right")
+        ax_1 = plot_1.axes.item()
+        plot_1.tight_layout()
+        ax_1.set_xlabel(r"$\sum p(x)^2$", fontsize=15)
+        ax_1.set_ylabel(r"$\sum p(x) \hat{p}(x)$", fontsize=15)
+        ax_1.set_title(r"Linear fit per cycle depth", fontsize=15)
+
+        plot_2 = sns.lmplot(
+            data=self.circuit_fidelities,
+            x="cycle_depth",
+            y="circuit_fidelity_estimate",
+            hue="cycle_depth",
+            palette="dark:r",
+        )
+        ax_2 = plot_2.axes.item()
+        plot_2.tight_layout()
+        ax_2.set_xlabel(r"Cycle depth", fontsize=15)
+        ax_2.set_ylabel(r"Circuit fidelity", fontsize=15)
+        ax_2.set_title(r"Exponential decay of circuit fidelity", fontsize=15)
+
+        # Add fit line
+        x = np.linspace(
+            self.circuit_fidelities.cycle_depth.min(), self.circuit_fidelities.cycle_depth.max()
+        )
+        y = self.results.cycle_fidelity_estimate**x
+        y_p = (self.results.cycle_fidelity_estimate + self.results.cycle_fidelity_estimate_std) ** x
+        y_m = (self.results.cycle_fidelity_estimate - self.results.cycle_fidelity_estimate_std) ** x
+        ax_2.plot(x, y, color="tab:red", linewidth=2)
+        ax_2.fill_between(x, y_m, y_p, alpha=0.2, color="tab:red")


Test patch:
diff --git a/supermarq-benchmarks/supermarq/qcvv/xeb_test.py b/supermarq-benchmarks/supermarq/qcvv/xeb_test.py
new file mode 100644
index 000000000..177d45aa6
--- /dev/null
+++ b/supermarq-benchmarks/supermarq/qcvv/xeb_test.py
@@ -0,0 +1,246 @@
+# Copyright 2021 The Cirq Developers
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# pylint: disable=missing-function-docstring
+# pylint: disable=missing-return-doc
+# mypy: disable-error-code=method-assign
+from __future__ import annotations
+
+import itertools
+import os
+from unittest.mock import MagicMock, patch
+
+import cirq
+import numpy as np
+import pandas as pd
+import pytest
+
+from supermarq.qcvv import XEB, XEBSample
+
+
+@pytest.fixture(scope="session", autouse=True)
+def patch_tqdm() -> None:
+    os.environ["TQDM_DISABLE"] = "1"
+
+
+def test_xeb_init() -> None:
+    with patch("cirq_superstaq.service.Service"):
+        experiment = XEB()
+        assert experiment.num_qubits == 2
+        assert experiment.two_qubit_gate == cirq.CZ
+        assert experiment.single_qubit_gate_set == [
+            cirq.PhasedXZGate(
+                z_exponent=z,
+                x_exponent=0.5,
+                axis_phase_exponent=a,
+            )
+            for a, z in itertools.product(np.linspace(start=0, stop=7 / 4, num=8), repeat=2)
+        ]
+
+        with pytest.raises(
+            RuntimeError, match="No samples to retrieve. The experiment has not been run."
+        ):
+            experiment.samples  # pylint: disable=W0104
+
+        with pytest.raises(
+            RuntimeError, match="No data to retrieve. The experiment has not been run."
+        ):
+            experiment.circuit_fidelities  # pylint: disable=W0104
+
+        experiment = XEB(two_qubit_gate=cirq.CX)
+        assert experiment.num_qubits == 2
+        assert experiment.two_qubit_gate == cirq.CX
+        assert experiment.single_qubit_gate_set == [
+            cirq.PhasedXZGate(
+                z_exponent=z,
+                x_exponent=0.5,
+                axis_phase_exponent=a,
+            )
+            for a, z in itertools.product(np.linspace(start=0, stop=7 / 4, num=8), repeat=2)
+        ]
+
+        experiment = XEB(single_qubit_gate_set=[cirq.X])
+        assert experiment.num_qubits == 2
+        assert experiment.two_qubit_gate == cirq.CZ
+        assert experiment.single_qubit_gate_set == [cirq.X]
+
+
+@pytest.fixture
+def xeb_experiment() -> XEB:
+    with patch("cirq_superstaq.service.Service"):
+        return XEB(single_qubit_gate_set=[cirq.X, cirq.Y, cirq.Z])
+
+
+def test_build_xeb_circuit(xeb_experiment: XEB) -> None:
+    with patch("supermarq.qcvv.xeb.random.choices") as random_choice:
+        random_choice.side_effect = [
+            [cirq.X, cirq.Y],
+            [cirq.Z, cirq.Y],
+            [cirq.Y, cirq.Z],
+            [cirq.X, cirq.Z],
+            [cirq.X, cirq.X],
+            [cirq.Y, cirq.Y],
+        ]
+        circuits = xeb_experiment._build_circuits(num_circuits=2, cycle_depths=[2])
+
+    assert len(circuits) == 2
+
+    qbs = xeb_experiment.qubits
+    cirq.testing.assert_same_circuits(
+        circuits[0].circuit,
+        cirq.Circuit(
+            [
+                cirq.X(qbs[0]),
+                cirq.Y(qbs[1]),
+                cirq.TaggedOperation(cirq.CZ(*qbs), "no_compile"),
+                cirq.Z(qbs[0]),
+                cirq.Y(qbs[1]),
+                cirq.TaggedOperation(cirq.CZ(*qbs), "no_compile"),
+                cirq.Y(qbs[0]),
+                cirq.Z(qbs[1]),
+                cirq.measure(qbs),
+            ]
+        ),
+    )
+    assert circuits[0].data == {"circuit_depth": 5, "num_cycles": 2, "two_qubit_gate": "CZ"}
+    cirq.testing.assert_same_circuits(
+        circuits[1].circuit,
+        cirq.Circuit(
+            [
+                cirq.X(qbs[0]),
+                cirq.Z(qbs[1]),
+                cirq.TaggedOperation(cirq.CZ(*qbs), "no_compile"),
+                cirq.X(qbs[0]),
+                cirq.X(qbs[1]),
+                cirq.TaggedOperation(cirq.CZ(*qbs), "no_compile"),
+                cirq.Y(qbs[0]),
+                cirq.Y(qbs[1]),
+                cirq.measure(qbs),
+            ]
+        ),
+    )
+    assert circuits[1].data == {"circuit_depth": 5, "num_cycles": 2, "two_qubit_gate": "CZ"}
+
+
+def test_xeb_analyse_results(xeb_experiment: XEB) -> None:
+    xeb_experiment._samples = MagicMock()
+    # Choose example data to give perfect fit with fidelity=0.95
+    xeb_experiment._raw_data = pd.DataFrame(
+        [
+            {
+                "cycle_depth": 1,
+                "circuit_depth": 3,
+                "sum_p(x)p(x)": 0.3,
+                "sum_p(x)p^(x)": 0.95**1 * 0.3,
+            },
+            {
+                "cycle_depth": 1,
+                "circuit_depth": 3,
+                "sum_p(x)p(x)": 0.5,
+                "sum_p(x)p^(x)": 0.95**1 * 0.5,
+            },
+            {
+                "cycle_depth": 5,
+                "circuit_depth": 11,
+                "sum_p(x)p(x)": 0.3,
+                "sum_p(x)p^(x)": 0.95**5 * 0.3,
+            },
+            {
+                "cycle_depth": 5,
+                "circuit_depth": 11,
+                "sum_p(x)p(x)": 0.5,
+                "sum_p(x)p^(x)": 0.95**5 * 0.5,
+            },
+            {
+                "cycle_depth": 10,
+                "circuit_depth": 21,
+                "sum_p(x)p(x)": 0.3,
+                "sum_p(x)p^(x)": 0.95**10 * 0.3,
+            },
+            {
+                "cycle_depth": 10,
+                "circuit_depth": 21,
+                "sum_p(x)p(x)": 0.5,
+                "sum_p(x)p^(x)": 0.95**10 * 0.5,
+            },
+        ]
+    )
+    results = xeb_experiment.analyze_results()
+
+    assert xeb_experiment.results.cycle_fidelity_estimate == pytest.approx(0.95)
+    assert xeb_experiment.results.cycle_fidelity_estimate_std == pytest.approx(0.0, abs=1e-8)
+
+    assert results == xeb_experiment.results
+
+    # Call plotting function to test no errors are raised.
+    xeb_experiment.plot_results()
+
+
+def test_xeb_process_probabilities(xeb_experiment: XEB) -> None:
+    qubits = cirq.LineQubit.range(2)
+
+    samples = [
+        XEBSample(
+            raw_circuit=cirq.Circuit(
+                [
+                    cirq.X(qubits[0]),
+                    cirq.X(qubits[1]),
+                    cirq.CX(qubits[0], qubits[1]),
+                    cirq.X(qubits[0]),
+                    cirq.X(qubits[1]),
+                    cirq.measure(qubits),
+                ]
+            ),
+            data={"circuit_depth": 3, "num_cycles": 1, "two_qubit_gate": "CX"},
+        )
+    ]
+    samples[0].probabilities = {"00": 0.1, "01": 0.3, "10": 0.4, "11": 0.2}
+
+    with patch("cirq.Simulator") as mock_simulator:
+        mock_simulator.return_value.simulate.return_value.final_state_vector = [0.0, 1.0, 0.0, 0.0]
+        data = xeb_experiment._process_probabilities(samples)
+
+    expected_data = pd.DataFrame(
+        [
+            {
+                "cycle_depth": 1,
+                "circuit_depth": 3,
+                "p(00)": 0.0,
+                "p(01)": 1.0,
+                "p(10)": 0.0,
+                "p(11)": 0.0,
+                "p^(00)": 0.1,
+                "p^(01)": 0.3,
+                "p^(10)": 0.4,
+                "p^(11)": 0.2,
+                "sum_p(x)p(x)": 1.0,
+                "sum_p(x)p^(x)": 0.3,
+            }
+        ]
+    )
+    pd.testing.assert_frame_equal(expected_data, data)
+
+
+def test_xebsample_sum_probs_square_no_values() -> None:
+    sample = XEBSample(raw_circuit=cirq.Circuit(), data={})
+    with pytest.raises(RuntimeError, match="`target_probabilities` have not yet been initialised"):
+        sample.sum_target_probs_square()
+
+
+def test_xebsample_sum_cross_sample_probs_no_values() -> None:
+    sample = XEBSample(raw_circuit=cirq.Circuit(), data={})
+    with pytest.raises(RuntimeError, match="`target_probabilities` have not yet been initialised"):
+        sample.sum_target_cross_sample_probs()
+
+    sample.target_probabilities = {"example": 0.6}
+    with pytest.raises(RuntimeError, match="`sample_probabilities` have not yet been initialised"):
+        sample.sum_target_cross_sample_probs()