TensoredExpvalMeasMitigator

class TensoredExpvalMeasMitigator(amats)

Bases: qiskit.ignis.mitigation.expval.base_meas_mitigator.BaseExpvalMeasMitigator

1-qubit tensor product measurement error mitigator.

This class can be used with the qiskit.ignis.mitigation.expectation_value() function to apply measurement error mitigation of local single-qubit measurement errors. Expectation values can also be computed directly using the expectation_value() method.

For measurement mitigation to be applied the mitigator should be calibrated using the qiskit.ignis.mitigation.expval_meas_mitigator_circuits() function and qiskit.ignis.mitigation.ExpvalMeasMitigatorFitter class with the 'tensored' mitigation method.

Initialize a TensorMeasurementMitigator

Parameters

amats (List[ndarray]) – list of single-qubit readout error assignment matrices.

Methods

assignment_fidelity

TensoredExpvalMeasMitigator.assignment_fidelity(qubits=None)

Return the measurement assignment fidelity on the specified qubits.

The assignment fidelity on N-qubits is defined as $\sum_{x\in\{0, 1\}^n} P(x|x) / 2^n$ , where $P(x|x) = \rangle x|A|x\langle$ , and $A$ is the assignment_matrix().

Parameters

qubits (Optional[List[int]]) – Optional, qubits being measured for operator expval.

Returns

the assignment fidelity.

Return type

float

assignment_matrix

TensoredExpvalMeasMitigator.assignment_matrix(qubits=None)

Return the measurement assignment matrix for specified qubits.

The assignment matrix is the stochastic matrix $A$ which assigns a noisy measurement probability distribution to an ideal input measurement distribution: $P(i|j) = \langle i | A | j \rangle$ .

Parameters

qubits (Optional[List[int]]) – Optional, qubits being measured for operator expval.

Returns

the assignment matrix A.

Return type

np.ndarray

expectation_value

TensoredExpvalMeasMitigator.expectation_value(counts, diagonal=None, qubits=None, clbits=None)

Compute the mitigated expectation value of a diagonal observable.

This computes the mitigated estimator of $\langle O \rangle = \text{Tr}[\rho. O]$ of a diagonal observable $O = \sum_{x\in\{0, 1\}^n} O(x)|x\rangle\!\langle x|$ .

Parameters

counts (Dict) – counts object
diagonal (Optional[ndarray]) – Optional, the vector of diagonal values for summing the expectation value. If None the the default value is $[1, -1]^\otimes n$ .
qubits (Optional[List[int]]) – Optional, the measured physical qubits the count bitstrings correspond to. If None qubits are assumed to be $[0, ..., n-1]$ .
clbits (Optional[List[int]]) – Optional, if not None marginalize counts to the specified bits.

Returns

the expectation value and standard deviation.

Return type

(float, float)

Additional Information:

The diagonal observable $O$ is input using the diagonal kwarg as a list or Numpy array $[O(0), ..., O(2^n -1)]$ . If no diagonal is specified the diagonal of the Pauli operator :math`O = mbox{diag}(Z^{otimes n}) = [1, -1]^{otimes n}` is used.

The clbits kwarg is used to marginalize the input counts dictionary over the specified bit-values, and the qubits kwarg is used to specify which physical qubits these bit-values correspond to as circuit.measure(qubits, clbits).

mitigation_matrix

TensoredExpvalMeasMitigator.mitigation_matrix(qubits=None)

Return the measurement mitigation matrix for the specified qubits.

The mitigation matrix $A^{-1}$ is defined as the inverse of the assignment_matrix() $A$ .

Parameters

qubits (Optional[List[int]]) – Optional, qubits being measured for operator expval.

Returns

the measurement error mitigation matrix $A^{-1}$ .

Return type

np.ndarray

mitigation_overhead

TensoredExpvalMeasMitigator.mitigation_overhead(qubits=None)

Return the mitigation overhead for expectation value estimation.

This is the multiplicative factor of extra shots required for estimating a mitigated expectation value with the same accuracy as an unmitigated expectation value.

Parameters

qubits (Optional[List[int]]) – Optional, qubits being measured for operator expval.

Returns

the mitigation overhead factor.

Return type

int

plot_assignment_matrix

TensoredExpvalMeasMitigator.plot_assignment_matrix(qubits=None, ax=None)

Matrix plot of the readout error assignment matrix.

Parameters

qubits (list(int)) – Optional, qubits being measured for operator expval.
ax (axes) – Optional. Axes object to add plot to.

Returns

the figure axes object.

Return type

plt.axes

Raises

ImportError – if matplotlib is not installed.

plot_mitigation_matrix

TensoredExpvalMeasMitigator.plot_mitigation_matrix(qubits=None, ax=None)

Matrix plot of the readout error mitigation matrix.

Parameters

qubits (list(int)) – Optional, qubits being measured for operator expval.
ax (plt.axes) – Optional. Axes object to add plot to.

Returns

the figure axes object.

Return type

plt.axes

Raises

ImportError – if matplotlib is not installed.

required_shots

TensoredExpvalMeasMitigator.required_shots(delta, qubits=None)

Return the number of shots required for expectation value estimation.

This is the number of shots required so that $|\langle O \rangle_{est} - \langle O \rangle_{true}| < \delta$ with high probability (at least 2/3) and is given by $4\delta^2 \Gamma^2$ where $\Gamma^2$ is the mitigation_overhead().

Parameters

delta (float) – Error tolerance for expectation value estimator.
qubits (Optional[List[int]]) – Optional, qubits being measured for operator expval.

Returns

the required shots.

Return type

int

stddev_upper_bound

TensoredExpvalMeasMitigator.stddev_upper_bound(shots=1, qubits=None)

Return an upper bound on standard deviation of expval estimator.

Parameters

shots (int) – Number of shots used for expectation value measurement.
qubits (Optional[List[int]]) – qubits being measured for operator expval.

Returns

the standard deviation upper bound.

Return type

float

Was this page helpful?

Report a bug or request content on GitHub.