104 lines
4.5 KiB
Plaintext
104 lines
4.5 KiB
Plaintext
---
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title: expval_meas_mitigator_circuits
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description: API reference for qiskit.ignis.mitigation.expval_meas_mitigator_circuits
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in_page_toc_min_heading_level: 1
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python_api_type: function
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python_api_name: qiskit.ignis.mitigation.expval_meas_mitigator_circuits
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---
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<span id="qiskit-ignis-mitigation-expval-meas-mitigator-circuits" />
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# qiskit.ignis.mitigation.expval\_meas\_mitigator\_circuits
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<Function id="qiskit.ignis.mitigation.expval_meas_mitigator_circuits" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-ignis/tree/stable/0.5/qiskit/ignis/mitigation/expval/circuits.py" signature="expval_meas_mitigator_circuits(num_qubits, method='CTMP', labels=None)">
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Generate measurement error mitigator circuits and metadata.
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Use the [`ExpvalMeasMitigatorFitter`](qiskit.ignis.mitigation.ExpvalMeasMitigatorFitter "qiskit.ignis.mitigation.ExpvalMeasMitigatorFitter") class to fit the execution results to construct a calibrated expectation value measurement error mitigator.
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**Parameters**
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* **num\_qubits** (`int`) – the number of qubits to calibrate.
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* **method** (`Optional`\[`str`]) – the mitigation method `'complete'`, `'tensored'`, or `'CTMP'`.
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* **labels** (`Optional`\[`List`\[`str`]]) – Optional, custom labels to run for calibration. If None the method will determine the default label values.
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**Returns**
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**(circuits, metadata) the measurement error characterization**
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circuits, and metadata for the fitter.
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**Return type**
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tuple
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## Mitigation Method:
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* The `'complete'` method will generate all $2^n$ computational basis states measurement circuits and fitting will return a [`CompleteExpvalMeasMitigator`](qiskit.ignis.mitigation.CompleteExpvalMeasMitigator "qiskit.ignis.mitigation.CompleteExpvalMeasMitigator"). This method should only be used for small numbers of qubits.
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* The `'tensored'` method will generate two input state circuits of the all 0 and all 1 states on number of qubits unless custom labels are specified. Ftting will return a [`TensoredExpvalMeasMitigator`](qiskit.ignis.mitigation.TensoredExpvalMeasMitigator "qiskit.ignis.mitigation.TensoredExpvalMeasMitigator"). This method assumes measurement errors are uncorrelated between qubits.
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* The `'CTMP'` method will generate $n+2$ input state circuits unless custom labels are specified. The default input states are the all 0 state, the all 1 state, and the $n$ state with a single qubit in the 1 state and all others in the 0 state. Ftting will return a [`CTMPExpvalMeasMitigator`](qiskit.ignis.mitigation.CTMPExpvalMeasMitigator "qiskit.ignis.mitigation.CTMPExpvalMeasMitigator").
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**Example**
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The following example shows calibrating a 5-qubit expectation value measurement error mitigator using the `'tensored'` method.
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```python
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from qiskit import execute
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from qiskit.test.mock import FakeVigo
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import qiskit.ignis.mitigation as mit
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backend = FakeVigo()
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num_qubits = backend.configuration().num_qubits
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# Generate calibration circuits
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circuits, metadata = mit.expval_meas_mitigator_circuits(
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num_qubits, method='tensored')
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result = execute(circuits, backend, shots=8192).result()
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# Fit mitigator
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mitigator = mit.ExpvalMeasMitigatorFitter(result, metadata).fit()
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# Plot fitted N-qubit assignment matrix
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mitigator.plot_assignment_matrix()
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```
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```python
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<matplotlib.axes._subplots.AxesSubplot at 0x7fc2ddc3c710>
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```
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The following shows how to use the above mitigator to apply measurement error mitigation to expectation value computations
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```python
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from qiskit import QuantumCircuit
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# Test Circuit with expectation value -1.
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qc = QuantumCircuit(num_qubits)
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qc.x(range(num_qubits))
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qc.measure_all()
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# Execute
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shots = 8192
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seed_simulator = 1999
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result = execute(qc, backend, shots=8192, seed_simulator=1999).result()
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counts = result.get_counts(0)
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# Expectation value of Z^N without mitigation
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expval_nomit, error_nomit = mit.expectation_value(counts)
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print('Expval (no mitigation): {:.2f} ± {:.2f}'.format(
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expval_nomit, error_nomit))
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# Expectation value of Z^N with mitigation
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expval_mit, error_mit = mit.expectation_value(counts,
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meas_mitigator=mitigator)
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print('Expval (with mitigation): {:.2f} ± {:.2f}'.format(
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expval_mit, error_mit))
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```
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```python
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Expval (no mitigation): -0.81 ± 0.01
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Expval (with mitigation): -0.99 ± 0.01
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```
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</Function>
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