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Error mitigation with Pennylane on IBMQ backends#

In this tutorial we will cover how to use Mitiq in conjunction with PennyLane, and further how to run error-mitigated circuits on IBMQ backends.

Setup: Defining a circuit#

For simplicity, we’ll use a single-qubit circuit with ten Pauli \(X\) gates that compiles to the identity, defined below.

import pennylane as qml

def circuit():
    for _ in range(10):
        qml.PauliX(wires=0)
    return qml.expval(qml.PauliZ(0))

In this example, we will use the probability of the ground state as our observable to mitigate, the expectation value of which should evaluate to one in the noiseless setting.

High-level usage#

As of version 0.19 of PennyLane, and 0.11 of Mitiq, PennyLane comes with out of the box support for error mitigation. This makes it very easy to use zero-noise extrapolation when working with PennyLane, regardless of where the circuit is being executed.

We define the executor function in the following code block. As we are using IBMQ backends, we first load our account.

Note

Using an IBM quantum computer requires a valid IBMQ account. See https://quantum-computing.ibm.com/ for instructions to create an account, save credentials, and get access to online quantum computers.

First, we get our devices set up depending on whether we would like to use real hardware, or a simulator.

import qiskit
from qiskit_ibm_provider import IBMProvider

USE_REAL_HARDWARE = False

if IBMProvider.saved_accounts() and USE_REAL_HARDWARE:
    provider = IBMProvider()
    dev = qml.device(
        "qiskit.ibmq",
        wires=1,
        backend="ibmq_qasm_simulator",
        provider=provider
    )
else:
    noise_strength = 0.05
    dev_noise_free = qml.device("default.mixed", wires=1)
    dev = qml.transforms.insert(
        dev_noise_free,
        qml.AmplitudeDamping,
        noise_strength
    )

With dev set to the desired device, we can now use PennyLane’s mitigate_with_zne function, in conjuction with a noise scaling method, and inference technique from Mitiq.

from mitiq.zne.scaling import fold_global
from mitiq.zne.inference import RichardsonFactory

scale_factors = [1, 2, 3]
noise_scale_method = fold_global

device_circuit = qml.QNode(circuit, dev)
error_mitigated_device_circuit = qml.transforms.mitigate_with_zne(
    device_circuit,
    scale_factors,
    noise_scale_method,
    RichardsonFactory.extrapolate,
)

We can now test the mitigated version of the circuit against the unmitigated to ensure it is working as expected.

unmitigated = device_circuit()
mitigated = error_mitigated_device_circuit()
print(f"Unmitigated result {unmitigated:.3f}")
print(f"Mitigated result   {mitigated:.3f}")

Unmitigated result 0.609
Mitigated result   0.937

As the ideal, desired result is 1.000, the mitigated result performs much better than unmitigated.

Options#

In this section we will discuss the many different options for noise scaling and extrapolation that can be passed into PennyLane’s mitigate_with_zne function.

The following code block shows an example of using linear extrapolation with five different (noise) scale factors.

from mitiq.zne.inference import LinearFactory

scale_factors = [1.0, 1.5, 2.0, 2.5, 3.0]
noise_scale_method = fold_global
mitigated = qml.transforms.mitigate_with_zne(
    device_circuit,
    scale_factors,
    noise_scale_method,
    LinearFactory.extrapolate,
)()

print(f"Mitigated result {mitigated:.3f}")

Mitigated result 0.787

To specify a different noise scaling method, we can pass a different function for the argument scale_noise. This function should input a circuit and scale factor and return a circuit. The following code block shows an example of scaling noise by local folding instead of global folding.

from mitiq.zne.scaling import fold_gates_at_random

scale_factors = [1.0, 1.5, 2.0, 2.5, 3.0]
noise_scale_method = fold_gates_at_random
mitigated = qml.transforms.mitigate_with_zne(
    device_circuit,
    scale_factors,
    noise_scale_method,
    LinearFactory.extrapolate,
)()

print(f"Mitigated result {mitigated:.3f}")

Mitigated result 0.782

Further options are described and elaborated in our article on additional options in ZNE.

Decorator usage#

Finally, it is perhaps more common to define a circuit using a decorator when you know in advance you would like an error mitigated value. For this our circuit will be defined as above, but we will use decorators to indicate which device we would like to run it on, and that we would like to error-mitigate it.

from mitiq.zne.scaling import fold_gates_from_left


@qml.qnode(dev)
def circuit():
    for _ in range(10):
        qml.PauliX(wires=0)
    return qml.expval(qml.PauliZ(0))

circuit = qml.transforms.mitigate_with_zne(
    circuit,
    [1, 2, 3],
    fold_gates_from_left,
    RichardsonFactory.extrapolate,
)

print(f"Zero-noise extrapolated value: {circuit():.3f}")

Zero-noise extrapolated value: 0.937

Finally, more information about using PennyLane together with Mitiq can be found in PennyLane’s tutorial on error mitigation.

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