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---
title: QVFitter (v0.29)
description: API reference for qiskit.ignis.verification.QVFitter in qiskit v0.29
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.ignis.verification.QVFitter
---
# QVFitter
<Class id="qiskit.ignis.verification.QVFitter" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-ignis/tree/stable/0.6/qiskit/ignis/verification/quantum_volume/fitters.py" signature="QVFitter(backend_result=None, statevector_result=None, qubit_lists=None)" modifiers="class">
Bases: `object`
Class for fitters for quantum volume.
**Parameters**
* **backend\_result** (*list*) list of results (qiskit.Result).
* **statevector\_result** (*list*) the ideal statevectors of each circuit
* **qubit\_lists** (*list*) list of qubit lists (what was passed to the circuit generation)
## Methods
<span id="qiskit-ignis-verification-qvfitter-add-data" />
### add\_data
<Function id="qiskit.ignis.verification.QVFitter.add_data" signature="QVFitter.add_data(new_backend_result, rerun_fit=True)">
Add a new result. Re calculate fit
**Parameters**
* **new\_backend\_result** (*list*) list of qv results
* **rerun\_fit** (*bool*) re calculate the means and fit the result
**Raises**
**QiskitError** If the ideal distribution isnt loaded yet
#### Additional information:
Assumes that result was executed is the output of circuits generated by qv\_circuits,
</Function>
<span id="qiskit-ignis-verification-qvfitter-add-statevectors" />
### add\_statevectors
<Function id="qiskit.ignis.verification.QVFitter.add_statevectors" signature="QVFitter.add_statevectors(new_statevector_result)">
Add the ideal results and convert to the heavy outputs.
Assume the result is from statevector\_simulator
**Parameters**
**new\_statevector\_result** (*list*) ideal results
**Raises**
**QiskitError** If the result has already been added for the circuit
</Function>
<span id="qiskit-ignis-verification-qvfitter-calc-confidence-level" />
### calc\_confidence\_level
<Function id="qiskit.ignis.verification.QVFitter.calc_confidence_level" signature="QVFitter.calc_confidence_level(z_value)">
Calculate confidence level using z value.
Accumulative probability for standard normal distribution in \[-z, +infinity] is 1/2 (1 + erf(z/sqrt(2))), where z = (X - mu)/sigma = (hmean - 2/3)/sigma
**Parameters**
**z\_value** (*float*) z value in in standard normal distibution.
**Returns**
confidence level in decimal (not percentage).
**Return type**
float
</Function>
<span id="qiskit-ignis-verification-qvfitter-calc-data" />
### calc\_data
<Function id="qiskit.ignis.verification.QVFitter.calc_data" signature="QVFitter.calc_data()">
Make a count dictionary for each unique circuit from all the results.
Calculate the heavy output probability.
#### Additional information:
Assumes that result was executed is the output of circuits generated by qv\_circuits,
</Function>
<span id="qiskit-ignis-verification-qvfitter-calc-statistics" />
### calc\_statistics
<Function id="qiskit.ignis.verification.QVFitter.calc_statistics" signature="QVFitter.calc_statistics()">
Convert the heavy outputs in the different trials into mean and error for plotting.
Here we assume the error is due to a binomial distribution. Error (standard deviation) for binomial distribution is sqrt(np(1-p)), where n is the number of trials (self.\_ntrials) and p is the success probability (self.\_ydata\[0]\[depthidx]/self.\_ntrials).
</Function>
<span id="qiskit-ignis-verification-qvfitter-calc-z-value" />
### calc\_z\_value
<Function id="qiskit.ignis.verification.QVFitter.calc_z_value" signature="QVFitter.calc_z_value(mean, sigma)">
Calculate z value using mean and sigma.
**Parameters**
* **mean** (*float*) mean
* **sigma** (*float*) standard deviation
**Returns**
z\_value in standard normal distibution.
**Return type**
float
</Function>
<span id="qiskit-ignis-verification-qvfitter-plot-hop-accumulative" />
### plot\_hop\_accumulative
<Function id="qiskit.ignis.verification.QVFitter.plot_hop_accumulative" signature="QVFitter.plot_hop_accumulative(depth, ax=None, figsize=(7, 5))">
Plot individual and accumulative heavy output probability (HOP) as a function of number of trials.
**Parameters**
* **depth** (*int*) depth of QV circuits
* **ax** (*Axes or None*) plot axis (if passed in).
* **figsize** (*tuple*) figure size in inches.
**Raises**
**ImportError** If matplotlib is not installed.
**Returns**
A figure of individual and accumulative HOP as a function of number of trials, with 2-sigma confidence interval and 2/3 threshold.
**Return type**
matplotlib.Figure
</Function>
<span id="qiskit-ignis-verification-qvfitter-plot-qv-data" />
### plot\_qv\_data
<Function id="qiskit.ignis.verification.QVFitter.plot_qv_data" signature="QVFitter.plot_qv_data(ax=None, show_plt=True, figsize=(7, 5), set_title=True, title=None)">
Plot the qv data as a function of depth
**Parameters**
* **ax** (*Axes or None*) plot axis (if passed in).
* **show\_plt** (*bool*) display the plot.
* **figsize** (*tuple*) Figure size in inches.
* **set\_title** (*bool*) set figure title.
* **title** (*String or None*) text for setting figure title
**Raises**
**ImportError** If matplotlib is not installed.
**Returns**
A figure of Quantum Volume data (heavy output probability) with two-sigma error bar as a function of circuit depth.
**Return type**
matplotlib.Figure
</Function>
<span id="qiskit-ignis-verification-qvfitter-plot-qv-trial" />
### plot\_qv\_trial
<Function id="qiskit.ignis.verification.QVFitter.plot_qv_trial" signature="QVFitter.plot_qv_trial(depth, trial_index, figsize=(7, 5), ax=None)">
Plot individual trial. :param depth: circuit depth :type depth: int :param trial\_index: trial index :type trial\_index: int :param figsize: Figure size in inches. :type figsize: tuple :param ax: plot axis (if passed in). :type ax: Axes or None
**Returns**
A figure for histogram of ideal and experiment probabilities.
**Return type**
matplotlib.Figure
</Function>
<span id="qiskit-ignis-verification-qvfitter-quantum-volume" />
### quantum\_volume
<Function id="qiskit.ignis.verification.QVFitter.quantum_volume" signature="QVFitter.quantum_volume()">
Return the volume for each depth.
**Returns**
List of quantum volumes
**Return type**
list
</Function>
<span id="qiskit-ignis-verification-qvfitter-qv-success" />
### qv\_success
<Function id="qiskit.ignis.verification.QVFitter.qv_success" signature="QVFitter.qv_success()">
Return whether each depth was successful (> 2/3 with confidence level > 0.977 corresponding to z\_value = 2) and the confidence level.
**Returns**
List of list of 2 elements for each depth: - success True/False - confidence level
**Return type**
list
</Function>
## Attributes
### depths
<Attribute id="qiskit.ignis.verification.QVFitter.depths">
Return depth list.
</Attribute>
### heavy\_output\_counts
<Attribute id="qiskit.ignis.verification.QVFitter.heavy_output_counts">
Return the number of heavy output counts as measured.
</Attribute>
### heavy\_output\_prob\_ideal
<Attribute id="qiskit.ignis.verification.QVFitter.heavy_output_prob_ideal">
Return the heavy output probability ideally.
</Attribute>
### heavy\_outputs
<Attribute id="qiskit.ignis.verification.QVFitter.heavy_outputs">
Return the ideal heavy outputs dictionary.
</Attribute>
### qubit\_lists
<Attribute id="qiskit.ignis.verification.QVFitter.qubit_lists">
Return depth list.
</Attribute>
### results
<Attribute id="qiskit.ignis.verification.QVFitter.results">
Return all the results.
</Attribute>
### ydata
<Attribute id="qiskit.ignis.verification.QVFitter.ydata">
Return the average and std of the output probability.
</Attribute>
</Class>