Add a tool for measuring expectation values of Pauli strings with readout error mitigation

cirq-core/cirq/contrib/paulistring/__init__.py

@@ -42,3 +42,7 @@
 )
 
 from cirq.contrib.paulistring.optimize import optimized_circuit as optimized_circuit
+
+from cirq.contrib.paulistring.pauli_string_measurement_with_readout_mitigation import (
+    measure_pauli_strings as measure_pauli_strings,
+)

cirq-core/cirq/contrib/paulistring/pauli_string_measurement_with_readout_mitigation.py

@@ -0,0 +1,267 @@
+# Copyright 2025 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.
+""" Tools for measuring expectation values of Pauli strings with readout error mitigation. """
+
+from typing import List, Tuple, Union
+
+import numpy as np
+
+from cirq import ops, circuits, work
+from cirq.contrib.shuffle_circuits import run_shuffled_with_readout_benchmarking
+from cirq.experiments import SingleQubitReadoutCalibrationResult
+from cirq.experiments.readout_confusion_matrix import TensoredConfusionMatrices
+from cirq.study import ResultDict
+
+
+def _pauli_string_to_basis_change_ops(
+    pauli_string: ops.PauliString, qid_list: list[ops.Qid]
+) -> List[ops.Operation]:
+    """Creates operations to change to the eigenbasis of the given Pauli string.
+
+    This function constructs a list of ops.Operation that performs basis changes
+    necessary to measure the given pauli_string in the computational basis.
+
+    Args:
+        pauli_string: The Pauli string to diagonalize.
+        qid_list: An ordered list of the qubits in the circuit.
+
+    Returns:
+        A list of Operations that, when applied before measurement in the
+        computational basis, effectively measures in the eigenbasis of
+        pauli_strings.
+    """
+    # ADD A TEST: GridQubit
+    operations = []
+    for qubit in pauli_string:
+        pauli_op = pauli_string[qubit]
+        qubit_index = qid_list.index(qubit)
+        if pauli_op == ops.X:
+            # Rotate to X basis: Ry(-pi/2)
+            operations.append(ops.ry(-np.pi / 2)(qid_list[qubit_index]))
+        elif pauli_op == ops.Y:
+            # Rotate to Y basis: Rx(pi/2)
+            operations.append(ops.rx(np.pi / 2)(qid_list[qubit_index]))
+        # No operation needed for Pauli Z or I (identity)
+    return operations
+
+
+def _build_one_qubit_confusion_matrix(e0: float, e1: float) -> np.ndarray:
+    """Builds a 2x2 confusion matrix for a single qubit.
+
+    Args:
+        e0: the 0->1 readout error rate.
+        e1: the 1->0 readout error rate.
+
+    Returns:
+        A 2x2 NumPy array representing the confusion matrix.
+    """
+    return np.array([[1 - e0, e1], [e0, 1 - e1]])
+
+
+def _build_many_one_qubits_confusion_matrix(
+    qubits_to_error: SingleQubitReadoutCalibrationResult,
+) -> list[np.ndarray]:
+    """Builds a list of confusion matrices from calibration results.
+
+    This function iterates through the calibration results for each qubit and
+    constructs a list of single-qubit confusion matrices.
+
+    Args:
+        qubits_to_error: An object containing calibration results for
+            single-qubit readout errors, including zero-state and one-state errors
+            for each qubit.
+
+    Returns:
+        A list of NumPy arrays, where each array is a 2x2 confusion matrix
+        for a qubit. The order of matrices corresponds to the order of qubits
+        in the calibration results (alphabetical order by qubit name).
+        Returns an empty list if no calibration results are provided.
+    """
+    cms = []
+    if not qubits_to_error:
+        return cms
+
+    for qubit in sorted(qubits_to_error.zero_state_errors.keys()):
+        e0 = qubits_to_error.zero_state_errors[qubit]
+        e1 = qubits_to_error.one_state_errors[qubit]
+        cms.append(_build_one_qubit_confusion_matrix(e0, e1))
+    return cms
+
+def _process_pauli_measurement_results(
+    qubits: List[ops.Qid],
+    pauli_strings: List[ops.PauliString],
+    circuit_results: List[ResultDict],
+    confusion_matrices: List[np.ndarray],
+    pauli_repetitions: int,
+    timestamp: float,
+) -> Tuple[List[Tuple[ops.PauliString, float, float]], List[Tuple[ops.PauliString, float, float]]]:
+    """Calculates both error-mitigated expectation values and unmitigated expectation values 
+    from measurement results.
+
+    This function takes the results from shuffled readout benchmarking and:
+    1. Builds confusion matrices from the calibration data.
+    2. Constructs a tensored confusion matrix for error mitigation, and one without.
+    3. Mitigates readout errors for each Pauli string measurement.
+    4. Calculates and returns both error-mitigated and unmitigated expectation values.
+
+    Args:
+        qubits: Qubits to build confusion matrices for. In a sorted order.
+        pauli_strings: The list of PauliStrings that were measured.
+        circuit_results: A list of ResultDict obtained
+            from running the Pauli measurement circuits.
+        confusion_matrices: A list of confusion matrices from calibration results. 
+        pauli_repetitions: The number of repetitions used for Pauli string measurements.
+        timestamp: The timestamp of the calibration results.
+
+    Returns:
+        A tuple containing:
+            - A list of tuples, where each tuple contains:
+                - The PauliString that was measured.
+                - The error-mitigated expectation value (float) of the Pauli string.
+                - The standard deviation of the error-mitigated expectation value.
+            - A list of tuples, where each tuple contains:
+                - The PauliString that was measured.
+                - The unmitigated expectation value (float) of the Pauli string.
+                - The standard deviation of the unmitigated expectation value.
+    """
+
+    tensored_cm = TensoredConfusionMatrices(
+        confusion_matrices,
+        [[q] for q in qubits],
+        repetitions=pauli_repetitions,
+        timestamp=timestamp,
+    )
+
+    exp_vals_with_mitigation = []
+    exp_vals_without_mitigation = []
+
+    for pauli_index, circuit_result in enumerate(circuit_results):
+        measurement_results = circuit_result.measurements["m"]
+
+        mitigated_values, d_m = tensored_cm.readout_mitigation_pauli_uncorrelated(
+            qubits, measurement_results
+        )
+
+        p1 = np.mean(np.sum(measurement_results, axis=1) % 2)
+        unmitigated_values = 1 - 2 * np.mean(p1)
+        d_unmit = 2 * np.sqrt(p1 * (1 - p1) / pauli_repetitions)
+
+        pauli_string = pauli_strings[pauli_index]
+        exp_vals_with_mitigation.append((pauli_string, mitigated_values, d_m))
+        exp_vals_without_mitigation.append((pauli_string, unmitigated_values, d_unmit))
+
+    return exp_vals_with_mitigation, exp_vals_without_mitigation
+
+def measure_pauli_strings(circuits_to_pauli: List[Tuple[circuits.Circuit, list[ops.PauliString]]],
+                          sampler: work.Sampler, rng_or_seed: Union[np.random.Generator, int],
+                          pauli_repetitions: int, readout_repetitions: int,
+                          num_random_bitstrings: int
+                          ) -> List[Tuple[circuits.Circuit,Tuple[
+                              List[Tuple[ops.PauliString, float, float]],
+                              List[Tuple[ops.PauliString, float, float]],
+                              SingleQubitReadoutCalibrationResult
+          ]],
+]:
+    """Measures expectation values of Pauli strings on given circuits with/without 
+    readout error mitigation.
+
+    This function takes a list of circuits and corresponding List[Pauli string] to measure.
+    For each circuit-List[Pauli string] pair, it:
+    1.  Constructs circuits to measure the Pauli string expectation value by
+        adding basis change moments and measurement operations.
+    2.  Runs shuffled readout benchmarking on these circuits to calibrate readout errors.
+    3.  Mitigates readout errors using the calibrated confusion matrices.
+    4.  Calculates and returns both error-mitigated and unmitigatedexpectation values for
+    each Pauli string.
+
+    Args:
+        circuits_to_pauli: A list of tuples, where each tuple contains:
+            - A Circuit to prepare the state for measurement.
+            - A list of PauliString objects to measure on the state
+              prepared by the circuit.
+        sampler: The sampler to use.
+        rng_or_seed: A random number generator or seed for the shuffled benchmarking.
+        pauli_repetitions: The number of repetitions for each circuit when measuring
+            Pauli strings.
+        readout_repetitions: The number of repetitions for readout calibration
+            in the shuffled benchmarking.
+        num_random_bitstrings: The number of random bitstrings to use in shuffled
+            benchmarking.
+
+    Returns:
+        A list of tuples, where each tuple corresponds to an input circuit and contains:
+            - The original input Circuit.
+            - A tuple containing:
+                - A list of tuples, where each tuple contains:
+                    - The `cirq.PauliString` that was measured.
+                    - The error-mitigated expectation value (float) of the Pauli string.
+                    - The standard deviation of the expectation value.
+                - A list of tuples, where each tuple contains:
+                    - The `cirq.PauliString` that was measured.
+                    - The unmitigated expectation value (float) of the Pauli string.
+                    - The standard deviation of the expectation value.
+                - Result of benchmarking readout error for the giving circuit.
+    """
+
+    # Extract unique qubit tuples from all circuits
+    input_circuits = [circuit for circuit, _ in circuits_to_pauli]
+
+    unique_qubit_tuples = set()
+    for circuit in input_circuits:
+        unique_qubit_tuples.add(tuple(sorted(set(circuit.all_qubits()))))
+    # qubits_list is a list of qubit tuples
+    qubits_list = sorted(unique_qubit_tuples)
+
+    pauli_measurement_circuits: List[circuits.Circuit] = []
+
+    # Build the basis-change circuits for each Pauli string
+    pauli_measurement_circuits = []
+    for input_circuit, pauli_strings in circuits_to_pauli:
+        qid_list = list(set(input_circuit.all_qubits()))
+        basis_change_circuits = []
+        for pauli_string in pauli_strings:
+            basis_change_circuit = input_circuit + _pauli_string_to_basis_change_ops(
+                pauli_string, qid_list) + ops.measure(*qid_list, key="m")
+            basis_change_circuits.append(basis_change_circuit)
+        pauli_measurement_circuits.extend(basis_change_circuits)
+
+    # Run shuffled benchmarking for readout calibration
+    circuits_results, calibration_results = run_shuffled_with_readout_benchmarking(
+        input_circuits=pauli_measurement_circuits,
+        sampler=sampler,
+        circuit_repetitions=pauli_repetitions,
+        rng_or_seed=rng_or_seed,
+        qubits=qubits_list,
+        num_random_bitstrings=num_random_bitstrings,
+        readout_repetitions=readout_repetitions,
+    )
+
+    # Process the results to calculate expectation values
+    results = []
+    circuit_result_index = 0
+    for input_circuit, pauli_strings in circuits_to_pauli:
+        qubits_in_circuit = tuple(sorted(set(input_circuit.all_qubits())))
+
+        confusion_matrices = _build_many_one_qubits_confusion_matrix(
+            calibration_results[qubits_in_circuit])
+        mitigated_res, unmitigated_res = _process_pauli_measurement_results(
+            qubits_in_circuit, pauli_strings,
+            circuits_results[circuit_result_index: circuit_result_index + len(pauli_strings)],
+            confusion_matrices, pauli_repetitions,
+            calibration_results[qubits_in_circuit].timestamp)
+        results.append((input_circuit, (mitigated_res, unmitigated_res,
+                                        calibration_results[qubits_in_circuit])))
+
+        circuit_result_index += len(pauli_strings)
+    return results