.. _basic: ########### Basic usage ########### Using M3 involves three steps (steps one and two can usually be done in reverse order if desired). 1) Select a system and calibrate over the desired set of qubits. 2) Run the circuit(s) of interest on the system. 3) Apply the readout correction and post-process. Simple example -------------- Here we use a noisy simulator to perform the three steps above. First we import the needed modules, and construct a circuit of interest. .. jupyter-execute:: import numpy as np 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() qc.draw('mpl') Next we calibrate an M3 mitigator instance over qubits 0 -> 6 (Step #1): .. jupyter-execute:: backend = FakeCasablanca() mit = mthree.M3Mitigation(backend) mit.cals_from_system(range(6)) Transpile and execute our circuit (Step #2): .. jupyter-execute:: trans_qc = transpile(qc, backend) raw_counts = backend.run(trans_qc).result().get_counts() Finally, apply the correction and post-process (Step #3). Here our post-processing is simply computing the expectation value from the returned quasi-probabilities: .. jupyter-execute:: quasis = mit.apply_correction(raw_counts, range(6)) print('Expectation value:',quasis.expval()) Specifying qubits to mitigate over ---------------------------------- The circuit above also fits on other systems without SWAP mapping provided that we select the correct layout. .. jupyter-execute:: from qiskit.test.mock.backends import FakeMontreal backend = FakeMontreal() mit2 = mthree.M3Mitigation(backend) In our case, ``qubits = [10, 12, 15, 13, 11, 14]`` is an appropriate layout. Importantly, the zeroth entry of the list tells us what physical qubit is readout to generate bit 0 in the output bit-strings. We must pass this list to both the calibration and correction steps of M3. .. jupyter-execute:: qubits = [10, 12, 15, 13, 11, 14] mit2.cals_from_system(qubits) trans_qc = transpile(qc, backend, initial_layout=qubits) raw_counts2 = backend.run(trans_qc).result().get_counts() quasis2 = mit2.apply_correction(raw_counts2, qubits) print('Expectation value:',quasis2.expval())