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Correcting probabilities

Shows how to correct readout errors and transform to true probability distribution.

[1]:
import numpy as np
from qiskit import *
from qiskit.test.mock import FakeMelbourne
from qiskit.visualization import plot_histogram
import mthree

Setup experiment

[2]:
backend = FakeMelbourne()
[3]:
qc = QuantumCircuit(5)
qc.h(2)
qc.cx(2,1)
qc.cx(1,0)
qc.cx(2,3)
qc.cx(3,4)
qc.measure_all()
qc.draw('mpl')
[3]:
../_images/tutorials_02_correcting_probs_4_0.png

Compile and run circuits and perform mitigation

[4]:
trans_qc = transpile(qc, backend)
[5]:
raw_counts = backend.run(trans_qc, shots=2048).result().get_counts()
[6]:
mit = mthree.M3Mitigation(backend)
mit.cals_from_system(range(qc.num_qubits))
[7]:
quasi = mit.apply_correction(raw_counts, range(qc.num_qubits))
probs = quasi.nearest_probability_distribution()

Plot result

[8]:
plot_histogram([raw_counts, probs], figsize=(10,4), legend=['Raw', 'M3'])
[8]:
../_images/tutorials_02_correcting_probs_11_0.png
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