Readout Mitigation¶
Readout errors affect quantum computation during the measurement of the qubits in a quantum device. By characterizing the readout errors, it is possible to construct a readout error mitigator that is used both to obtain a more accurate distribution of the outputs, and more accurate measurements of expectation value for measurables.
The readout mitigator is generated from an assignment matrix: a \(2^n \times 2^n\) matrix \(A\) such that \(A_{y,x}\) is the probability to observe \(y\) given the true outcome should be \(x\). The assignment matrix is used to compute the mitigation matrix used in the readout error mitigation process itself.
A Local readout mitigator works under the assumption that readout
errors are mostly local, meaning readout errors for different qubits
are independent of each other. In this case, the assignment matrix is
the tensor product of \(n\) \(2 \times 2\) matrices, one for
each qubit, making it practical to store the assignment matrix in
implicit form, by storing the individual \(2 \times 2\) assignment
matrices. The corresponding class in Qiskit is the Local readout
mitigator
in qiskit-terra.
A Correlated readout mitigator uses the full \(2^n \times 2^n\)
assignment matrix, meaning it can only be used for small values of
\(n\). The corresponding class in Qiskit is the Correlated readout
mitigator
in qiskit-terra.
This notebook demonstrates the usage of both the local and correlated experiments to generate the corresponding mitigators.
import numpy as np
import matplotlib.pyplot as plt
from qiskit import QuantumCircuit
from qiskit.visualization import plot_histogram
from qiskit_experiments.library import LocalReadoutError, CorrelatedReadoutError
# For simulation
from qiskit.providers.aer import AerSimulator
from qiskit.providers.fake_provider import FakeParis
from qiskit.result.mitigation.utils import (
expval_with_stddev,
str2diag,
counts_probability_vector
)
backend = AerSimulator.from_backend(FakeParis())
SHOTS = 1024
qubits = [0,1,2,3]
num_qubits = len(qubits)
Standard mitigation experiment¶
The default mitigation experiment is local, meaning error probability is measured individually for each qubit. The experiment generates two circuits, one for all “0” and one for all “1” results.
exp = LocalReadoutError(qubits)
for c in exp.circuits():
print(c)
░ ┌─┐
q_0: ─░─┤M├─────────
░ └╥┘┌─┐
q_1: ─░──╫─┤M├──────
░ ║ └╥┘┌─┐
q_2: ─░──╫──╫─┤M├───
░ ║ ║ └╥┘┌─┐
q_3: ─░──╫──╫──╫─┤M├
░ ║ ║ ║ └╥┘
meas: 4/════╩══╩══╩══╩═
0 1 2 3
┌───┐ ░ ┌─┐
q_0: ┤ X ├─░─┤M├─────────
├───┤ ░ └╥┘┌─┐
q_1: ┤ X ├─░──╫─┤M├──────
├───┤ ░ ║ └╥┘┌─┐
q_2: ┤ X ├─░──╫──╫─┤M├───
├───┤ ░ ║ ║ └╥┘┌─┐
q_3: ┤ X ├─░──╫──╫──╫─┤M├
└───┘ ░ ║ ║ ║ └╥┘
meas: 4/═════════╩══╩══╩══╩═
0 1 2 3
exp.analysis.set_options(plot=True)
result = exp.run(backend)
mitigator = result.analysis_results(0).value
The resulting measurement matrix can be illustrated by comparing it to the identity.
result.figure(0)
Mitigation matrices¶
The individual mitigation matrices can be read off the mitigator.
for m in mitigator._mitigation_mats:
print(m)
print()
[[ 1.00506586 -0.03242148]
[-0.00506586 1.03242148]]
[[ 1.0070922 -0.03039514]
[-0.0070922 1.03039514]]
[[ 1.01519757 -0.02228977]
[-0.01519757 1.02228977]]
[[ 1.00199601 -0.01996008]
[-0.00199601 1.01996008]]
Mitigation Example¶
qc = QuantumCircuit(num_qubits)
qc.h(0)
for i in range(1, num_qubits):
qc.cx(i - 1, i)
qc.measure_all()
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()}
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())
Probabilities¶
legend = ['Mitigated Probabilities', 'Unmitigated Probabilities']
plot_histogram([mitigated_probs, unmitigated_probs], legend=legend, sort="value_desc", bar_labels=False)
Expectation value¶
diagonal_labels = ["ZZZZ", "ZIZI", "IZII", "1ZZ0"]
ideal_expectation = []
diagonals = [str2diag(d) for d in diagonal_labels]
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]
mitigated_expectation = [mitigator.expectation_value(counts, d) for d in diagonals]
mitigated_expectation_values, mitigated_stddev = zip(*mitigated_expectation)
unmitigated_expectation_values, unmitigated_stddev = zip(*unmitigated_expectation)
legend = ['Mitigated Expectation', 'Unmitigated Expectation']
fig, ax = plt.subplots()
X = np.arange(4)
ax.bar(X + 0.00, mitigated_expectation_values, yerr=mitigated_stddev, color='b', width = 0.25, label="Mitigated Expectation")
ax.bar(X + 0.25, unmitigated_expectation_values, yerr=unmitigated_stddev, color='g', width = 0.25, label="Unmitigated Expectation")
ax.set_xticks([0.125 + i for i in range(len(diagonals))])
ax.set_xticklabels(diagonal_labels)
ax.legend()
<matplotlib.legend.Legend at 0x7f419c6801c0>
