113 lines
4.1 KiB
Plaintext
113 lines
4.1 KiB
Plaintext
---
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title: TensoredMeasFitter
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description: API reference for qiskit.ignis.mitigation.TensoredMeasFitter
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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.ignis.mitigation.TensoredMeasFitter
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---
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# TensoredMeasFitter
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<Class id="qiskit.ignis.mitigation.TensoredMeasFitter" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-ignis/tree/stable/0.6/qiskit/ignis/mitigation/measurement/fitters.py" signature="TensoredMeasFitter(results, mit_pattern, substate_labels_list=None, circlabel='')" modifiers="class">
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Bases: `object`
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Measurement correction fitter for a tensored calibration.
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Initialize a measurement calibration matrix from the results of running the circuits returned by measurement\_calibration\_circuits.
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**Parameters**
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* **results** (`Union`\[`Result`, `List`\[`Result`]]) – the results of running the measurement calibration circuits. If this is None, the user will set calibration matrices later.
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* **mit\_pattern** (`List`\[`List`\[`int`]]) – qubits to perform the measurement correction on, divided to groups according to tensors
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* **substate\_labels\_list** (`Optional`\[`List`\[`List`\[`str`]]]) – for each calibration matrix, the labels of its rows and columns. If None, the labels are ordered lexicographically
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* **circlabel** (`str`) – if the qubits were labeled
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**Raises**
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**ValueError** – if the mit\_pattern doesn’t match the substate\_labels\_list
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## Methods
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### add\_data
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<Function id="qiskit.ignis.mitigation.TensoredMeasFitter.add_data" signature="TensoredMeasFitter.add_data(new_results, rebuild_cal_matrix=True)">
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Add measurement calibration data
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**Parameters**
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* **new\_results** (*list or* [*qiskit.result.Result*](qiskit.result.Result "qiskit.result.Result")) – a single result or list of Result objects.
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* **rebuild\_cal\_matrix** (*bool*) – rebuild the calibration matrix
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</Function>
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### plot\_calibration
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<Function id="qiskit.ignis.mitigation.TensoredMeasFitter.plot_calibration" signature="TensoredMeasFitter.plot_calibration(cal_index=0, ax=None, show_plot=True)">
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Plot one of the calibration matrices (2D color grid plot).
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**Parameters**
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* **cal\_index** (*integer*) – calibration matrix to plot
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* **ax** (*matplotlib.axes*) – settings for the graph
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* **show\_plot** (*bool*) – call plt.show()
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**Raises**
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* **QiskitError** – if \_cal\_matrices was not set.
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* **ImportError** – if matplotlib was not installed.
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</Function>
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### readout\_fidelity
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<Function id="qiskit.ignis.mitigation.TensoredMeasFitter.readout_fidelity" signature="TensoredMeasFitter.readout_fidelity(cal_index=0, label_list=None)">
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Based on the results, output the readout fidelity, which is the average of the diagonal entries in the calibration matrices.
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**Parameters**
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* **cal\_index** (*integer*) – readout fidelity for this index in \_cal\_matrices
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* **label\_list** (*list*) – Returns the average fidelity over of the groups f states. In the form of a list of lists of states. If None, then each state used in the construction of the calibration matrices forms a group of size 1
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**Returns**
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The readout fidelity (assignment fidelity)
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**Return type**
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numpy.array
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**Raises**
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**QiskitError** – If the calibration matrix has not been set for the object.
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#### Additional Information:
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The on-diagonal elements of the calibration matrices are the probabilities of measuring state ‘x’ given preparation of state ‘x’.
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</Function>
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## Attributes
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### cal\_matrices
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<Attribute id="qiskit.ignis.mitigation.TensoredMeasFitter.cal_matrices">
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Return cal\_matrices.
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</Attribute>
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### filter
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<Attribute id="qiskit.ignis.mitigation.TensoredMeasFitter.filter">
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Return a measurement filter using the cal matrices.
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</Attribute>
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### nqubits
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<Attribute id="qiskit.ignis.mitigation.TensoredMeasFitter.nqubits">
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Return \_qubit\_list\_sizes.
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</Attribute>
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### substate\_labels\_list
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<Attribute id="qiskit.ignis.mitigation.TensoredMeasFitter.substate_labels_list">
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Return \_substate\_labels\_list.
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</Attribute>
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</Class>
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