# Error mitigation on IBMQ backends¶

This tutorial shows an example of how to mitigate noise on IBMQ backends, broken down in the following steps.

## Setup: Defining a circuit¶

First we import Qiskit and mitiq.

import qiskit
import mitiq
from mitiq.mitiq_qiskit.qiskit_utils import random_identity_circuit


For simplicity, we’ll use a random single-qubit circuit with ten gates that compiles to the identity, defined below.

>>> circuit = random_identity_circuit(depth=10)
>>> print(circuit)
┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐
q_0: |0>┤ Y ├┤ Y ├┤ X ├┤ Z ├┤ Z ├┤ Z ├┤ Z ├┤ X ├┤ X ├┤ Z ├┤ Y ├
└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘
c_0: 0 ═══════════════════════════════════════════════════════


Currently this circuit has no measurements, but we will add a measurement below and use the probability of the ground state as our observable to mitigate.

## High-level usage¶

To use mitiq with just a few lines of code, we simply need to define a function which inputs a circuit and outputs the expectation value to mitigate. This function will:

1. [Optionally] Add measurement(s) to the circuit.

2. Run the circuit.

3. Convert from raw measurement statistics (or a different output format) to an expectation value.

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

Note

The following code requires a valid IBMQ account. See https://quantum-computing.ibm.com/ for instructions.

provider = qiskit.IBMQ.load_account()

def armonk_executor(circuit: qiskit.QuantumCircuit, shots: int = 1024) -> float:
"""Returns the expectation value to be mitigated.

Args:
circuit: Circuit to run.
shots: Number of times to execute the circuit to compute the expectation value.
"""
# (1) Add measurements to the circuit
circuit.measure(circuit.qregs[0], circuit.cregs[0])

# (2) Run the circuit
job = qiskit.execute(
experiments=circuit,
# Change backend=provider.get_backend("ibmq_armonk") to run on hardware
backend=provider.get_backend("ibmq_qasm_simulator"),
optimization_level=0,  # Important!
shots=shots
)

# (3) Convert from raw measurement counts to the expectation value
counts = job.result().get_counts()
if counts.get("0") is None:
expectation_value = 0.
else:
expectation_value = counts.get("0") / shots
return expectation_value


At this point, the circuit can be executed to return a mitigated expectation value by running mitiq.execute_with_zne, as follows.

mitigated = mitiq.execute_with_zne(circuit, armonk_executor)


As long as a circuit and a function for executing the circuit are defined, the mitiq.execute_with_zne function can be called as above to return zero-noise extrapolated expectation value(s).

### Options¶

Different options for noise scaling and extrapolation can be passed into the mitiq.execute_with_zne function. By default, noise is scaled by locally folding gates at random, and the default extrapolation is Richardson.

To specify a different extrapolation technique, we can pass a different Factory object to execute_with_zne. The following code block shows an example of using linear extrapolation with five different (noise) scale factors.

linear_factory = mitiq.zne.inference.LinearFactory(scale_factors=[1.0, 1.5, 2.0, 2.5, 3.0])
mitigated = mitiq.execute_with_zne(circuit, armonk_executor, fac=linear_factory)


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 folding gates starting from the left (instead of at random, the default behavior for mitiq.execute_with_zne).

mitigated = mitiq.execute_with_zne(circuit, armonk_executor, scale_noise=mitiq.zne.scaling.fold_gates_from_left)


Any different combination of noise scaling and extrapolation technique can be passed as arguments to mitiq.execute_with_zne.

### Cirq frontend¶

It isn’t necessary to use Qiskit frontends (circuits) to run on IBM backends. We can use conversions in mitiq to use any supported frontend with any supported backend. Below, we show how to run a Cirq circuit on an IBMQ backend.

First, we define the Cirq circuit.

import cirq

qbit = cirq.GridQubit(0, 0)
cirq_circuit = cirq.Circuit(cirq.ops.H.on(qbit)


Now, we simply add a line to our executor function which converts from a Cirq circuit to a Qiskit circuit.

from mitiq.mitiq_qiskit.conversions import to_qiskit

def cirq_armonk_executor(cirq_circuit: cirq.Circuit, shots: int = 1024) -> float:
qiskit_circuit = to_qiskit(cirq_circuit)
return armonk_executor(qiskit_circuit, shots)


After this, we can use mitiq.execute_with_zne in the same way as above.

mitigated = mitiq.execute_with_zne(cirq_circuit, cirq_armonk_executor)


As above, different noise scaling or extrapolation methods can be used.

## Lower-level usage¶

Here, we give more detailed usage of the mitiq library which mimics what happens in the call to mitiq.execute_with_zne in the previous example. In addition to showing more of the mitiq library, this example explains the code in the previous section in more detail.

First, we define factors to scale the circuit length by and fold the circuit using the fold_gates_at_random local folding method.

depth = 10
circuit = random_identity_circuit(depth=depth)

scale_factors = [1., 1.5, 2., 2.5, 3.]
folded_circuits = [
mitiq.zne.scaling.fold_local(
circuit, scale, method=mitiq.zne.scaling.fold_gates_at_random
) for scale in scale_factors
]


We now add the observables we want to measure to the circuit. Here we use a single observable $$\Pi_0 \equiv |0\rangle \langle0|$$ – i.e., the probability of measuring the ground state – but other observables can be used.

for folded_circuit in folded_circuits:
folded_circuit.measure(folded_circuit.qregs[0], folded_circuit.cregs[0])


For a noiseless simulation, the expectation of this observable should be 1.0 because our circuit compiles to the identity. For noisy simulation, the value will be smaller than one. Because folding introduces more gates and thus more noise, the expectation value will decrease as the length (scale factor) of the folded circuits increase. By fitting this to a curve, we can extrapolate to the zero-noise limit and obtain a better estimate.

In the code block below, we setup our connection to IBMQ backends.

Note

The following code requires a valid IBMQ account. See https://quantum-computing.ibm.com/ for instructions.

provider = qiskit.IBMQ.load_account()
print("Available backends:", *provider.backends(), sep="\n")


Depending on your IBMQ account, this print statement will display different available backend names. Shown below is an example of executing the folded circuits using the IBMQ Armonk single qubit backend. Depending on what backends are available, you may wish to choose a different backend by changing the backend_name below.

shots = 8192
backend_name = "ibmq_armonk"

job = qiskit.execute(
experiments=folded_circuits,
# Change backend=provider.get_backend(backend_name) to run on hardware
backend=provider.get_backend("ibmq_qasm_simulator"),
optimization_level=0,  # Important!
shots=shots
)


Note

We set the optimization_level=0 to prevent any compilation by Qiskit transpilers.

Once the job has finished executing, we can convert the raw measurement statistics to observable values by running the following code block.

all_counts = [job.result().get_counts(i) for i in range(len(folded_circuits))]
expectation_values = [counts.get("0") / shots for counts in all_counts]


We can now see the unmitigated observable value by printing the first element of expectation_values. (This value corresponds to a circuit with scale factor one, i.e., the original circuit.)

>>> print("Unmitigated expectation value:", round(expectation_values[0], 3))
Unmitigated expectation value: 0.945


Now we can use the reduce method of mitiq.Factory objects to extrapolate to the zero-noise limit. Below we use a linear fit (order one polynomial fit) and print out the extrapolated zero-noise value.

>>> fac = mitiq.zne.inference.LinearFactory(scale_factors)
>>> fac.instack, fac.outstack = scale_factors, expectation_values
>>> zero_noise_value = fac.reduce()
>>> print(f"Extrapolated zero-noise value:", round(zero_noise_value, 3))
Extrapolated zero-noise value: 0.961


For this example, we indeed see that the extrapolated zero-noise value (0.961) is closer to the true value (1.0) than the unmitigated expectation value (0.945).