Adopt qiskit.result.mitigation into qiskit_experiments.data_processing

docs/manuals/measurement/readout_mitigation.rst

@@ -47,25 +47,16 @@ experiments to generate the corresponding mitigators.
     import numpy as np
     import matplotlib.pyplot as plt
     from qiskit import QuantumCircuit
+    from qiskit.quantum_info import Operator
     from qiskit.visualization import plot_distribution
+    from qiskit_experiments.data_processing import LocalReadoutMitigator
     from qiskit_experiments.library import LocalReadoutError, CorrelatedReadoutError
 
     from qiskit_aer import AerSimulator
     from qiskit_ibm_runtime.fake_provider import FakePerth
 
-    from qiskit.result.mitigation.utils import (
-        expval_with_stddev,
-        str2diag,
-        counts_probability_vector
-    )
-
     backend = AerSimulator.from_backend(FakePerth())
 
-.. jupyter-execute::
-
-    shots = 1024
-    qubits = [0,1,2,3]
-    num_qubits = len(qubits)
 
 Standard mitigation experiment
 ------------------------------
@@ -76,13 +67,14 @@ circuits, one for all “0” and one for all “1” results.
 
 .. jupyter-execute::
 
+    shots = 1024
+    qubits = [0,1,2,3]
+    num_qubits = len(qubits)
+
     exp = LocalReadoutError(qubits)
     for c in exp.circuits():
         print(c)
 
-
-.. jupyter-execute::
-
     exp.analysis.set_options(plot=True)
     result = exp.run(backend)
     mitigator = result.analysis_results("Local Readout Mitigator").value
@@ -102,9 +94,9 @@ The individual mitigation matrices can be read off the mitigator.
 
 .. jupyter-execute::
 
-    for m in mitigator._mitigation_mats:
-        print(m)
-        print()
+    for qubit in mitigator.qubits:
+        print(f"Qubit: {qubit}")
+        print(mitigator.mitigation_matrix(qubits=qubit))
 
 
 Mitigation example
@@ -118,13 +110,9 @@ Mitigation example
         qc.cx(i - 1, i)
     qc.measure_all()
 
-.. jupyter-execute::
-
     counts = backend.run(qc, shots=shots, seed_simulator=42, method="density_matrix").result().get_counts()
     unmitigated_probs = {label: count / shots for label, count in counts.items()}
 
-.. jupyter-execute::
-
     mitigated_quasi_probs = mitigator.quasi_probabilities(counts)
     mitigated_stddev = mitigated_quasi_probs._stddev_upper_bound
     mitigated_probs = (mitigated_quasi_probs.nearest_probability_distribution().binary_probabilities())
@@ -144,15 +132,20 @@ Expectation value
 .. jupyter-execute::
 
     diagonal_labels = ["ZZZZ", "ZIZI", "IZII", "1ZZ0"]
-    ideal_expectation = []
-    diagonals = [str2diag(d) for d in diagonal_labels]
+    diagonals = [
+        np.diag(np.real(Operator.from_label(d).to_matrix()))
+        for d in diagonal_labels
+    ]
+
+    # Create a mitigator with no mitigation so that we can use its
+    # expectation_values method to generate an unmitigated expectation value to
+    # compare to the mitigated one.
+    identity_mitigator = LocalReadoutMitigator([np.eye(2) for _ in range(4)])
+
     qubit_index = {i: i for i in range(num_qubits)}
-    unmitigated_probs_vector, _ = counts_probability_vector(unmitigated_probs, qubit_index=qubit_index)
-    unmitigated_expectation = [expval_with_stddev(d, unmitigated_probs_vector, shots) for d in diagonals]
+    unmitigated_expectation = [identity_mitigator.expectation_value(counts, d) for d in diagonals]
     mitigated_expectation = [mitigator.expectation_value(counts, d) for d in diagonals]
 
-.. jupyter-execute::
-
     mitigated_expectation_values, mitigated_stddev = zip(*mitigated_expectation)
     unmitigated_expectation_values, unmitigated_stddev = zip(*unmitigated_expectation)
     legend = ['Mitigated Expectation', 'Unmitigated Expectation']

qiskit_experiments/data_processing/__init__.py

@@ -82,6 +82,15 @@
     BaseDiscriminator
     SkLDA
     SkQDA
+
+Mitigators
+==========
+.. autosummary::
+    :toctree: ../stubs/
+
+    BaseReadoutMitigator
+    LocalReadoutMitigator
+    CorrelatedReadoutMitigator
 """
 
 from .data_action import DataAction, TrainableDataAction
@@ -104,4 +113,7 @@
 
 from .data_processor import DataProcessor
 from .discriminator import BaseDiscriminator
+from .mitigation.base_readout_mitigator import BaseReadoutMitigator
+from .mitigation.correlated_readout_mitigator import CorrelatedReadoutMitigator
+from .mitigation.local_readout_mitigator import LocalReadoutMitigator
 from .sklearn_discriminators import SkLDA, SkQDA

qiskit_experiments/data_processing/mitigation/__init__.py

@@ -0,0 +1,22 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2017, 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Readout error mitigation."""
+from .base_readout_mitigator import BaseReadoutMitigator
+from .correlated_readout_mitigator import CorrelatedReadoutMitigator
+from .local_readout_mitigator import LocalReadoutMitigator
+from .utils import (
+    counts_probability_vector,
+    expval_with_stddev,
+    stddev,
+    str2diag,
+)

qiskit_experiments/data_processing/mitigation/base_readout_mitigator.py

@@ -0,0 +1,81 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+Base class for readout error mitigation.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Optional, List, Iterable, Tuple, Union, Callable
+
+import numpy as np
+
+from qiskit.result.counts import Counts
+from qiskit.result.distributions.quasi import QuasiDistribution
+
+
+class BaseReadoutMitigator(ABC):
+    """Base readout error mitigator class."""
+
+    @abstractmethod
+    def quasi_probabilities(
+        self,
+        data: Counts,
+        qubits: Iterable[int] = None,
+        clbits: Optional[List[int]] = None,
+        shots: Optional[int] = None,
+    ) -> QuasiDistribution:
+        """Convert counts to a dictionary of quasi-probabilities
+
+        Args:
+            data: Counts to be mitigated.
+            qubits: the physical qubits measured to obtain the counts clbits.
+                If None these are assumed to be qubits [0, ..., N-1]
+                for N-bit counts.
+            clbits: Optional, marginalize counts to just these bits.
+            shots: Optional, the total number of shots, if None shots will
+                be calculated as the sum of all counts.
+
+        Returns:
+            QuasiDistribution: A dictionary containing pairs of [output, mean] where "output"
+                is the key in the dictionaries,
+                which is the length-N bitstring of a measured standard basis state,
+                and "mean" is the mean of non-zero quasi-probability estimates.
+        """
+
+    @abstractmethod
+    def expectation_value(
+        self,
+        data: Counts,
+        diagonal: Union[Callable, dict, str, np.ndarray],
+        qubits: Iterable[int] = None,
+        clbits: Optional[List[int]] = None,
+        shots: Optional[int] = None,
+    ) -> Tuple[float, float]:
+        """Calculate the expectation value of a diagonal Hermitian operator.
+
+        Args:
+            data: Counts object to be mitigated.
+            diagonal: the diagonal operator. This may either be specified
+                      as a string containing I,Z,0,1 characters, or as a
+                      real valued 1D array_like object supplying the full diagonal,
+                      or as a dictionary, or as Callable.
+            qubits: the physical qubits measured to obtain the counts clbits.
+                    If None these are assumed to be qubits [0, ..., N-1]
+                    for N-bit counts.
+            clbits: Optional, marginalize counts to just these bits.
+            shots: Optional, the total number of shots, if None shots will
+                be calculated as the sum of all counts.
+
+        Returns:
+            The mean and an upper bound of the standard deviation of operator
+            expectation value calculated from the current counts.
+        """