Readout Calibration (RCAL)

This example illustrates how to generate readout calibration (RCAL) circuits and use them to correct readout errors in other circuits. While this example uses a simulator to execute the circuits, the same procedure can be followed for hardware applications.

The code below generates RCAL circuits for qubits 0 and 1, populates their results using a simulator with readout error, and displays their confusion matrices.

import trueq as tq

# generate RCAL circuits to measure the readout errors on qubits [0, 1]
circuits = tq.make_rcal([0, 1])

# initialize a simulator with a 20% readout error on every qubit
sim = tq.Simulator().add_readout_error(0.2)

# run the circuits on the simulator to populate their results, n_shots=1000)

# display the confusion matrices
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Readout Calibration
(0,) P(0 | 0) = 0.792
P(1 | 1) = 0.815
Confusion Matrix:
0.792 0.185 0.208 0.815
  • labels: (0, 1)
  • protocol: RCAL
(1,) P(0 | 0) = 0.812
P(1 | 1) = 0.800
Confusion Matrix:
0.812 0.200 0.188 0.800
  • labels: (0, 1)
  • protocol: RCAL

Automatic Correction

If your circuit collection contains RCAL circuits, then that information will be used automatically to apply readout correction to your circuits when you call fit() or plot(). The code below illustrates this for SRB circuits executed on a noisy simulator with readout error. It also shows how you can view the results with and without readout correction being applied.

# generate RCAL circuits to measure the readout errors on qubits [0, 1, 2]
circuits = tq.make_rcal([0, 1, 2])

# generate SRB circuits to simultaneously characterize a single qubit [0] and
# a pair of qubits [1, 2] with 30 circuits for each random cycle in [4, 32, 64]
circuits.append(tq.make_srb([[0], [1, 2]], [4, 32, 64], 30))

# initialize a noisy simulator with a large 10% readout error
sim = tq.Simulator().add_stochastic_pauli(px=0.01).add_readout_error(0.1)

# RCAL generally needs more shots than the other protocols because it is estimating
# an absolute value rather than a decay over randomizations, thus in this simulation
# we use different amounts of shots for SRB and RCAL to populate their results["RCAL"]), n_shots=50)"RCAL"), n_shots=1000)

# plot the exponential decay with readout correction,
# where each expectation value (dot) has been compensated
SRB on [0], SRB on [1, 2]

To avoid performing readout correction during analysis, we can remove all RCAL circuits from the collection before calling plot(). Notice that the y-intercept is lower than in the plot above.

# plot the exponential decay without readout correction
SRB on [0], SRB on [1, 2]

Total running time of the script: ( 0 minutes 0.885 seconds)

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