LogicalQ.Analysis

Functions

circuit_scaling_bar3d(data[, title, save, filename, ...])	Plot a three-dimensional bar chart comparing qubit count and circuit length to expectation value.
noise_scaling_scatter(all_data[, scan_keys, ...])	Accepts the results of a noise_scaling_experiment and plots in bar graph / scatter plot format.
noise_scaling_Bloch_sphere(all_data[, plot_metric, ...])
qec_cycle_efficiency_scatter(all_data[, scan_keys, ...])
counts_to_statevector(counts)
calculate_state_probability(state, counts)
calculate_exp_val(counts)	Computes expectation value from circuit measurement counts.

Module Contents

LogicalQ.Analysis.circuit_scaling_bar3d(data, title=None, save=False, filename=None, save_dir=None, show=False)

Plot a three-dimensional bar chart comparing qubit count and circuit length to expectation value.

Parameters:

• data (dict[n_qubits, dict[circuit_length, (result, counts)]])

• title (str) – Plot title

• save (bool) – If true, output plot is saved

• filename (str) – Filename to be saved as, if save is True

• save_dir (str) – Directory to be saved in, if save is True

• show (str) – If true, output plot is displayed

Returns:

plt – A matplotlib plot object

Return type:

matplotlib.pyplot

LogicalQ.Analysis.noise_scaling_scatter(all_data, scan_keys=None, separate_plots=False, save=False, filename=None, save_dir=None, show=False)

Accepts the results of a noise_scaling_experiment and plots in bar graph / scatter plot format.

Parameters:

all_data – Output of a noise_scaling_experiment() run.

Returns:

plt

LogicalQ.Analysis.noise_scaling_Bloch_sphere(all_data, plot_metric=None, save=False, filename=None, save_dir=None, show=False)

LogicalQ.Analysis.qec_cycle_efficiency_scatter(all_data, scan_keys=None, plot_metric=None, show=False)

LogicalQ.Analysis.counts_to_statevector(counts)

LogicalQ.Analysis.calculate_state_probability(state, counts)

LogicalQ.Analysis.calculate_exp_val(counts)

Computes expectation value from circuit measurement counts.