QVFitter
class QVFitter(backend_result=None, statevector_result=None, qubit_lists=None)
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)
Attributes
depths
Return depth list.
heavy_output_counts
Return the number of heavy output counts as measured.
heavy_output_prob_ideal
Return the heavy output probability ideally.
heavy_outputs
Return the ideal heavy outputs dictionary.
qubit_lists
Return depth list.
results
Return all the results.
ydata
Return the average and std of the output probability.
Methods
add_data
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 isn’t loaded yet
Additional information:
Assumes that ‘result’ was executed is the output of circuits generated by qv_circuits,
add_statevectors
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
calc_data
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,
calc_statistics
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
plot_qv_data
QVFitter.plot_qv_data(ax=None, show_plt=True)
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.
Raises
ImportError – If matplotlib is not installed.
quantum_volume
QVFitter.quantum_volume()
Return the volume for each depth.
Returns
List of quantum volumes
Return type
list
qv_success
QVFitter.qv_success()
Return whether each depth was successful (>2/3 with confidence greater than 97.5) and the confidence
Returns
List of lenth depth with eact element a 3 list with - success True/False - confidence
Return type
list