qiskit.visualization.plot_distribution

qiskit.visualization.plot_distribution(data, figsize=(7, 5), color=None, number_to_keep=None, sort='asc', target_string=None, legend=None, bar_labels=True, title=None, ax=None, filename=None)

Plot a distribution from input sampled data.

Parameters

• data (list ordict) – This is either a list of dictionaries or a single dict containing the values to represent (ex {‘001’: 130})
• figsize (tuple) – Figure size in inches.
• color (list orstr) – String or list of strings for distribution bar colors.
• number_to_keep (int) – The number of terms to plot per dataset. The rest is made into a single bar called ‘rest’. If multiple datasets are given, the number_to_keep applies to each dataset individually, which may result in more bars than number_to_keep + 1. The number_to_keep applies to the total values, rather than the x-axis sort.
• sort (string) – Could be ‘asc’, ‘desc’, ‘hamming’, ‘value’, or ‘value_desc’. If set to ‘value’ or ‘value_desc’ the x axis will be sorted by the maximum probability for each bitstring. Defaults to ‘asc’.
• target_string (str) – Target string if ‘sort’ is a distance measure.
• legend (list) – A list of strings to use for labels of the data. The number of entries must match the length of data (if data is a list or 1 if it’s a dict)
• bar_labels (bool) – Label each bar in histogram with probability value.
• title (str) – A string to use for the plot title
• ax (matplotlib.axes.Axes) – An optional Axes object to be used for the visualization output. If none is specified a new matplotlib Figure will be created and used. Additionally, if specified there will be no returned Figure since it is redundant.
• filename (str) – file path to save image to.

Returns

A figure for the rendered distribution, if the ax kwarg is not set.

Return type

matplotlib.Figure

Raises

• MissingOptionalLibraryError – Matplotlib not available.
• VisualizationError – When legend is provided and the length doesn’t match the input data.

Examples

# Plot two counts in the same figure with legends and colors specified.
 
from qiskit.visualization import plot_distribution
 
counts1 = {'00': 525, '11': 499}
counts2 = {'00': 511, '11': 514}
 
legend = ['First execution', 'Second execution']
 
plot_distribution([counts1, counts2], legend=legend, color=['crimson','midnightblue'],
                title="New Distribution")
 
# You can sort the bitstrings using different methods.
 
counts = {'001': 596, '011': 211, '010': 50, '000': 117, '101': 33, '111': 8,
        '100': 6, '110': 3}
 
# Sort by the counts in descending order
dist1 = plot_distribution(counts, sort='value_desc')
 
# Sort by the hamming distance (the number of bit flips to change from
# one bitstring to the other) from a target string.
dist2 = plot_distribution(counts, sort='hamming', target_string='001')