Qiskit Pocket Guide

Chapter 3. Visualizing Quantum Measurements and States

In addition to drawing circuits (see “Drawing a quantum circuit”), Qiskit provides visualizations for data such as measurement counts and quantum states.

Visualizing Measurement Counts

To visualize experiments that result in measurement counts, Qiskit contains the plot_histogram() function.

Using the plot_histogram Function

The plot_histogram() function takes a dictionary containing measurement counts and plots them in a bar graph with one bar per basis state. We’ll demonstrate this function in Example 3-1 by plotting the measurement counts from the example in “Using the AerSimulator to hold measurement results”.

Example 3-1. Using the plot_histogram() function to plot measurement counts

from qiskit import QuantumCircuit,Aer,transpile
from qiskit.visualization import plot_histogram

qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()

backend = Aer.get_backend("aer_simulator")
tqc = transpile(qc, backend)
job = backend.run(tqc, shots=1000)
result = job.result()
counts = result.get_counts(tqc)

plot_histogram(counts)

Figure 3-1 shows the counts expressed as probabilities in a bar graph.

Figure 3-1. Example bar graph using the plot_histogram() function

Table 3-1 contains a list of commonly used plot_histogram parameters. This is implemented with matplotlib, which uses parameters shown in the table, such as figsize