163 lines
6.5 KiB
Plaintext
163 lines
6.5 KiB
Plaintext
---
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title: CorrelatedReadoutMitigator
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description: API reference for qiskit.result.CorrelatedReadoutMitigator
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.result.CorrelatedReadoutMitigator
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---
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# CorrelatedReadoutMitigator
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<Class id="qiskit.result.CorrelatedReadoutMitigator" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.23/qiskit/result/mitigation/correlated_readout_mitigator.py" signature="CorrelatedReadoutMitigator(assignment_matrix, qubits=None)" modifiers="class">
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Bases: [`qiskit.result.mitigation.base_readout_mitigator.BaseReadoutMitigator`](qiskit.result.BaseReadoutMitigator "qiskit.result.mitigation.base_readout_mitigator.BaseReadoutMitigator")
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N-qubit readout error mitigator.
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Mitigates [`expectation_value()`](qiskit.result.CorrelatedReadoutMitigator#expectation_value "qiskit.result.CorrelatedReadoutMitigator.expectation_value") and [`quasi_probabilities()`](qiskit.result.CorrelatedReadoutMitigator#quasi_probabilities "qiskit.result.CorrelatedReadoutMitigator.quasi_probabilities"). The mitigation\_matrix should be calibrated using qiskit experiments. This mitigation method should be used in case the readout errors of the qubits are assumed to be correlated. The mitigation\_matrix of *N* qubits is of size $2^N x 2^N$ so the mitigation complexity is $O(4^N)$.
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Initialize a CorrelatedReadoutMitigator
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**Parameters**
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* **assignment\_matrix** (`ndarray`) – readout error assignment matrix.
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* **qubits** (`Optional`\[`Iterable`\[`int`]]) – Optional, the measured physical qubits for mitigation.
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**Raises**
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**QiskitError** – matrix size does not agree with number of qubits
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## Methods
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### assignment\_matrix
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<Function id="qiskit.result.CorrelatedReadoutMitigator.assignment_matrix" signature="CorrelatedReadoutMitigator.assignment_matrix(qubits=None)">
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Return the readout assignment matrix for specified qubits.
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The assignment matrix is the stochastic matrix $A$ which assigns a noisy readout probability distribution to an ideal input readout distribution: $P(i|j) = \langle i | A | j \rangle$.
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**Parameters**
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**qubits** (`Optional`\[`List`\[`int`]]) – Optional, qubits being measured.
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**Returns**
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the assignment matrix A.
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**Return type**
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np.ndarray
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</Function>
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### expectation\_value
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<Function id="qiskit.result.CorrelatedReadoutMitigator.expectation_value" signature="CorrelatedReadoutMitigator.expectation_value(data, diagonal=None, qubits=None, clbits=None, shots=None)">
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Compute the mitigated expectation value of a diagonal observable.
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This computes the mitigated estimator of $\langle O \rangle = \mbox{Tr}[\rho. O]$ of a diagonal observable $O = \sum_{x\in\{0, 1\}^n} O(x)|x\rangle\!\langle x|$.
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**Parameters**
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* **data** ([`Counts`](qiskit.result.Counts "qiskit.result.counts.Counts")) – Counts object
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* **diagonal** (`Union`\[`Callable`, `dict`, `str`, `ndarray`, `None`]) – Optional, the vector of diagonal values for summing the expectation value. If `None` the the default value is $[1, -1]^\otimes n$.
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* **qubits** (`Optional`\[`Iterable`\[`int`]]) – Optional, the measured physical qubits the count bitstrings correspond to. If None qubits are assumed to be $[0, ..., n-1]$.
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* **clbits** (`Optional`\[`List`\[`int`]]) – Optional, if not None marginalize counts to the specified bits.
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* **shots** (`Optional`\[`int`]) – the number of shots.
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**Returns**
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the expectation value and an upper bound of the standard deviation.
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**Return type**
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(float, float)
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#### Additional Information:
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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)`.
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</Function>
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### mitigation\_matrix
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<Function id="qiskit.result.CorrelatedReadoutMitigator.mitigation_matrix" signature="CorrelatedReadoutMitigator.mitigation_matrix(qubits=None)">
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Return the readout mitigation matrix for the specified qubits.
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The mitigation matrix $A^{-1}$ is defined as the inverse of the [`assignment_matrix()`](qiskit.result.CorrelatedReadoutMitigator#assignment_matrix "qiskit.result.CorrelatedReadoutMitigator.assignment_matrix") $A$.
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**Parameters**
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**qubits** (`Optional`\[`List`\[`int`]]) – Optional, qubits being measured.
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**Returns**
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the measurement error mitigation matrix $A^{-1}$.
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**Return type**
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np.ndarray
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</Function>
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### quasi\_probabilities
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<Function id="qiskit.result.CorrelatedReadoutMitigator.quasi_probabilities" signature="CorrelatedReadoutMitigator.quasi_probabilities(data, qubits=None, clbits=None, shots=None)">
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Compute mitigated quasi probabilities value.
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**Parameters**
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* **data** ([`Counts`](qiskit.result.Counts "qiskit.result.counts.Counts")) – counts object
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* **qubits** (`Optional`\[`List`\[`int`]]) – qubits the count bitstrings correspond to.
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* **clbits** (`Optional`\[`List`\[`int`]]) – Optional, marginalize counts to just these bits.
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* **shots** (`Optional`\[`int`]) – Optional, the total number of shots, if None shots will be calculated as the sum of all counts.
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**Returns**
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**A dictionary containing pairs of \[output, mean] where “output”**
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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.
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**Return type**
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QuasiDistibution
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</Function>
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### stddev\_upper\_bound
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<Function id="qiskit.result.CorrelatedReadoutMitigator.stddev_upper_bound" signature="CorrelatedReadoutMitigator.stddev_upper_bound(shots)">
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Return an upper bound on standard deviation of expval estimator.
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**Parameters**
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**shots** (`int`) – Number of shots used for expectation value measurement.
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**Returns**
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the standard deviation upper bound.
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**Return type**
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float
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</Function>
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## Attributes
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### qubits
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<Attribute id="qiskit.result.CorrelatedReadoutMitigator.qubits">
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The device qubits for this mitigator
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**Return type**
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`Tuple`\[`int`]
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</Attribute>
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### settings
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<Attribute id="qiskit.result.CorrelatedReadoutMitigator.settings">
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Return settings.
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**Return type**
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`Dict`
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</Attribute>
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</Class>
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