LogicalQ.Analysis
=================

.. py:module:: LogicalQ.Analysis


Functions
---------

.. autoapisummary::

   LogicalQ.Analysis.circuit_scaling_bar3d
   LogicalQ.Analysis.noise_scaling_scatter
   LogicalQ.Analysis.noise_scaling_Bloch_sphere
   LogicalQ.Analysis.qec_cycle_efficiency_scatter
   LogicalQ.Analysis.counts_to_statevector
   LogicalQ.Analysis.calculate_state_probability
   LogicalQ.Analysis.calculate_exp_val


Module Contents
---------------

.. py:function:: 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.

   :param data:
   :type data: dict[n_qubits, dict[circuit_length, (result, counts)]]
   :param title: Plot title
   :type title: str
   :param save: If true, output plot is saved
   :type save: bool
   :param filename: Filename to be saved as, if save is True
   :type filename: str
   :param save_dir: Directory to be saved in, if save is True
   :type save_dir: str
   :param show: If true, output plot is displayed
   :type show: str

   :returns: **plt** -- A matplotlib plot object
   :rtype: matplotlib.pyplot


.. py:function:: 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.

   :param all_data: Output of a noise_scaling_experiment() run.

   :returns: plt


.. py:function:: noise_scaling_Bloch_sphere(all_data, plot_metric=None, save=False, filename=None, save_dir=None, show=False)

.. py:function:: qec_cycle_efficiency_scatter(all_data, scan_keys=None, plot_metric=None, show=False)

.. py:function:: counts_to_statevector(counts)

.. py:function:: calculate_state_probability(state, counts)

.. py:function:: calculate_exp_val(counts)

   Computes expectation value from circuit measurement counts.