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# Example: Running Stochastic Calibration

This example will show how to characterize the noise acting on a system so that it can be corrected. We begin by initializing a noisy simulator with a 2-qubit rotation about \(Z\) by some angle \(\theta\). Then we show how to find \(\theta\) using stochastic calibration.

```
[2]:
```

```
import numpy as np
import trueq as tq
# make a noisy simulator
sim = tq.Simulator().add_depolarizing(p=0.001)
sim.add_overrotation(single_sys=0.01, multi_sys=0.01)
# adding a rotation by 12 degrees to the first qubit in every 2-qubit gate,
# i.e. Z(12) is the noise we are going to try to characterize
mat = tq.Gate.from_generators("Z", 12).mat
sim.add_kraus([np.kron(mat, np.eye(2))])
```

```
[2]:
```

```
<trueq.simulation.simulator.Simulator at 0x7f2db14d4f50>
```

We are going to perform stochastic calibration for a \(CZ\) gate. We choose to look at the \(XI\) Pauli decay because it anticommutes with our suspected error, \(ZI\). Equivalently, we could have chosen to use the \(YI\) Pauli decay to characterize our \(ZI\) error. To minimize the experimental footprint, we use data at a single sequence length as in [15].

```
[3]:
```

```
cycle = tq.Cycle({(0, 1): tq.Gate.cz})
# generate SC circuits with 24 random cycles to get decays associated with XI
circuits = tq.make_sc(cycle, [24], pauli_decays=["XI"])
print(len(circuits))
```

```
30
```

Find the expectation value and standard deviation of the circuit when corrections of the form \(Z(\phi)\) are applied on the first qubit prior to each \(CZ\) gate. The expectation values correspond to the \(XI\) diagonal entry of the superoperator in the Pauli basis; values close to \(1\) indicate that the suspected error is small.

```
[4]:
```

```
# 20 equidistant points between -40 and 40 for trial values of phi
angles = np.linspace(-40, 40, 20)
all_circuits = tq.CircuitCollection()
for j, phi in enumerate(angles):
# adds a Z(phi) rotation on qubit 0 gate before every CZ gate
c = tq.compilation.CycleReplacement(
cycle, replacement=[tq.Cycle({(0): tq.Gate.from_generators("Z", phi)}), cycle]
)
new_circs = tq.CircuitCollection(map(c.apply, circuits))
# run circuit collection (with Z(phi)s inserted)
sim.run(new_circs)
# put all circuits into one callection, organized by the custom keyword "phi"
all_circuits.append(new_circs.update_keys(phi=phi))
```

Plot the expectation values as a function of \(\phi\):

```
[5]:
```

```
all_circuits.plot.compare("f_24_XI", "phi")
```

The y-axis is the expectation value after 24 (randomized) applications of the cycle of interest, and the x-axis is the correction angle we have compiled into the circuit. The maximum expectation value corresponds to the value of \(XI\) closest to 1, so finding the angle at which the plot peaks tells us which angle to rotate by to correct the noise. The peak occurs at approximately \(\phi = -12\), which is consistent with the noise applied by the simulator.

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