226 lines
6.9 KiB
Plaintext
226 lines
6.9 KiB
Plaintext
---
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title: BaseCoherenceFitter
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description: API reference for qiskit.ignis.characterization.BaseCoherenceFitter
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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.characterization.BaseCoherenceFitter
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---
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# BaseCoherenceFitter
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<Class id="qiskit.ignis.characterization.BaseCoherenceFitter" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-ignis/tree/stable/0.6/qiskit/ignis/characterization/fitters.py" signature="BaseCoherenceFitter(description, backend_result, xdata, qubits, fit_fun, fit_p0, fit_bounds, circuit_names, series=None, expected_state='0', time_index=0, time_unit='micro-seconds')" modifiers="class">
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Bases: `qiskit.ignis.characterization.fitters.BaseFitter`
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Base class for fitters of characteristic times
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**Parameters**
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* **description** (`str`) – description of the fitter’s purpose, e.g. ‘T1’.
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* **backend\_result** (`Union`\[`Result`, `List`\[`Result`]]) – result of execution on the backend.
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* **xdata** (`Union`\[`List`\[`float`], `array`]) – delay times of the circuits.
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* **qubits** (`List`\[`int`]) – the qubits to be characterized.
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* **fit\_fun** (`Callable`\[…, `float`]) – equivalent to parameter f of scipy.curve\_fit.
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* **fit\_p0** (`List`\[`float`]) – equivalent to parameter p0 of scipy.curve\_fit.
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* **fit\_bounds** (`Tuple`\[`List`\[`float`], `List`\[`float`]]) – equivalent to parameter bounds of scipy.curve\_fit.
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* **circuit\_names** (`List`\[`str`]) – names of the circuits, should be the same length as xdata. Full circuit name will be these plus the series name.
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* **series** (`Optional`\[`List`\[`str`]]) – list of circuit name tags
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* **expected\_state** (`str`) – is the circuit supposed to end up in ‘0’ or ‘1’?
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* **time\_index** (`int`) – among parameters of fit\_fun, which one is the characteristic time.
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* **time\_unit** (`str`) – unit of delay times in xdata.
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## Methods
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### add\_data
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<Function id="qiskit.ignis.characterization.BaseCoherenceFitter.add_data" signature="BaseCoherenceFitter.add_data(results, recalc=True, refit=True)">
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Add new execution results to previous execution results
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**Parameters**
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* **results** (`Union`\[`Result`, `List`\[`Result`]]) – new execution results
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* **recalc** (`bool`) – whether tp recalculate the data
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* **refit** (`bool`) – whether to refit the data
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</Function>
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### fit\_data
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<Function id="qiskit.ignis.characterization.BaseCoherenceFitter.fit_data" signature="BaseCoherenceFitter.fit_data(qid=- 1, p0=None, bounds=None, series=None)">
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Fit the curve.
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Compute self.\_params and self.\_params\_err
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**Parameters**
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* **qid** (`int`) – qubit for fitting. If -1 fit for all the qubits
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* **p0** (`Optional`\[`List`\[`float`]]) – initial guess, equivalent to p0 in scipy.optimize
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* **bounds** (`Optional`\[`Tuple`\[`List`\[`float`], `List`\[`float`]]]) – bounds, equivalent to bounds in scipy.optimize
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* **series** (`Optional`\[`str`]) – series to fit (if None fit all)
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</Function>
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### plot
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<Function id="qiskit.ignis.characterization.BaseCoherenceFitter.plot" signature="BaseCoherenceFitter.plot(qind, series, ax=None, show_plot=True)">
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Plot coherence data.
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**Parameters**
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* **qind** (`int`) – qubit index to plot
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* **series** (`str`) – which series to plot (if list then plot multiple)
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* **ax** (`Optional`\[`Any`]) – plot axes
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* **show\_plot** (`bool`) – whether to call plt.show()
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**Returns**
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The axes object
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**Return type**
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Axes
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**Raises**
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**ImportError** – if matplotlib is not installed
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</Function>
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### time
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<Function id="qiskit.ignis.characterization.BaseCoherenceFitter.time" signature="BaseCoherenceFitter.time(qid=- 1, series='0')">
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Return the characteristic time for the given qubit and series
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**Parameters**
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* **qid** (`int`) – the qubit index (or all qubits if -1)
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* **series** (`str`) – the series to get
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**Return type**
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`Union`\[`float`, `List`\[`float`]]
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**Returns**
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The characteristic time of the qubit, or all qubits
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</Function>
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### time\_err
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<Function id="qiskit.ignis.characterization.BaseCoherenceFitter.time_err" signature="BaseCoherenceFitter.time_err(qid=- 1, series='0')">
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Return the error of characteristic time for the given qubit and series
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**Parameters**
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* **qid** (`int`) – the qubit index (or all qubits if -1)
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* **series** (`str`) – the series to get
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**Return type**
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`Union`\[`float`, `List`\[`float`]]
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**Returns**
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The error of the characteristic time of the qubit, or all qubits
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</Function>
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## Attributes
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### backend\_result
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.backend_result">
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Return the execution results
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**Return type**
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`Union`\[`Result`, `List`\[`Result`]]
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</Attribute>
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### description
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.description">
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Return the fitter’s purpose, e.g. ‘T1’
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**Return type**
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`str`
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</Attribute>
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### fit\_fun
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.fit_fun">
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Return the function used in the fit, e.g. BaseFitter.\_exp\_fit\_fun
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**Return type**
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`Callable`
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</Attribute>
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### measured\_qubits
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.measured_qubits">
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Return the indices of the qubits to be characterized
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**Return type**
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`List`\[`int`]
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</Attribute>
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### params
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.params">
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Return the fit function parameters that were calculated by curve\_fit
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**Return type**
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`List`\[`float`]
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</Attribute>
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### params\_err
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.params_err">
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Return the error of the fit function parameters
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**Return type**
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`List`\[`float`]
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</Attribute>
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### series
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.series">
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Return the list of series for the data
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**Return type**
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`Optional`\[`List`\[`str`]]
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</Attribute>
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### xdata
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.xdata">
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Return the data points on the x-axis, the independenet parameter which is fit against
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**Return type**
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`Union`\[`List`\[`float`], `array`]
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</Attribute>
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### ydata
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<Attribute id="qiskit.ignis.characterization.BaseCoherenceFitter.ydata">
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Return the data points on the y-axis
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The data points are returning in the form of a list of dictionaries:
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> * **ydata\[i]\[‘mean’] is a list, where item**
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>
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> no. j is the probability of success of qubit i for a circuit that lasts xdata\[j].
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>
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> * **ydata\[i]\[‘std’] is a list, where ydata\[‘std’]\[j] is the**
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>
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> standard deviation of the success of qubit i.
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**Return type**
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`List`\[`Dict`]
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</Attribute>
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</Class>
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