Using distribution collectionsΒΆ
When you mitigate over multiple circuits the return object is a mthree.classes.QuasiCollection
from qiskit import *
from qiskit.test.mock.backends import FakeCasablanca
import mthree
qc = QuantumCircuit(6)
qc.reset(range(6))
qc.h(3)
qc.cx(3,1)
qc.cx(3,5)
qc.cx(1,0)
qc.cx(5,4)
qc.cx(1,2)
qc.measure_all()
backend = FakeCasablanca()
mit = mthree.M3Mitigation(backend)
mit.cals_from_system(range(6))
trans_qc = transpile([qc]*10, backend)
raw_counts = backend.run(trans_qc, shots=4000).result().get_counts()
quasis = mit.apply_correction(raw_counts, range(6), return_mitigation_overhead=True)
type(quasis)
mthree.classes.QuasiCollection
QuasiCollection
objects allow one to work with multiple distributions in the same manner as
a single one. E.g. we can get the mitigation overhead of the whole collection
quasis.mitigation_overhead
array([1.82864975, 1.836114 , 1.82514205, 1.81609206, 1.81970266,
1.84039375, 1.83450865, 1.84131901, 1.8438245 , 1.84860439])
or compute expectation values and standard deviations over the full set:
quasis.expval_and_stddev('IZIZIZ')
[(0.031889988863480934, 0.0213813572696495),
(0.015011617234746644, 0.021424950437280973),
(0.04824896769976994, 0.021360840652538716),
(0.061388693858312804, 0.0213078158287979),
(0.022039226978772364, 0.021328986472460783),
(0.016174724824145892, 0.021449905290659235),
(0.04468546755385622, 0.021415582206691722),
(-0.017935556219499327, 0.021455296594062756),
(0.012894241820297137, 0.021469888789445783),
(0.03773860346902552, 0.021497699831827206)]