# Error mitigation with Cirq on IBMQ backends#

This tutorial shows an example of how to mitigate noise on IBMQ backends with the Cirq frontend. It isn’t necessary to use Qiskit frontends (circuits) to run on IBM backends. We can use conversions in Mitiq to use supported frontends with supported backends. Below, we show how to run a Cirq circuit on an IBMQ backend.

## Settings#

```
from mitiq import zne
from mitiq.interface.mitiq_qiskit.qiskit_utils import initialized_depolarizing_noise
USE_REAL_HARDWARE = False
```

**Note:** When `USE_REAL_HARDWARE`

is set to `False`

, a classically simulated noisy backend is used instead of a real quantum computer.

## Setup: Defining a circuit in Cirq#

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

```
import cirq
qbit = cirq.LineQubit(0)
cirq_circuit = cirq.Circuit([cirq.X(qbit)] * 10, cirq.measure(qbit))
print(cirq_circuit)
```

```
0: ───X───X───X───X───X───X───X───X───X───X───M───
```

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#

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

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

Run the circuit.

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

For information on how to define more advanced executors, see the Executors section of the Mitiq user guide.

We define the executor function in the following code block. Because 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 see online quantum computers.

```
import qiskit
from qiskit_aer import QasmSimulator
from qiskit_ibm_runtime import QiskitRuntimeService
from mitiq.interface.mitiq_qiskit.conversions import to_qiskit
if QiskitRuntimeService.saved_accounts() and USE_REAL_HARDWARE:
service = QiskitRuntimeService()
backend = service.least_busy(operational=True, simulator=False)
noise_model = False
else:
# Simulate the circuit with noise
noise_model = initialized_depolarizing_noise(noise_level=0.02)
# Default to a simulator.
backend = QasmSimulator(noise_model=noise_model)
def cirq_ibm_executor(cirq_circuit: cirq.Circuit, 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.
"""
# Convert from Cirq circuit to Qiskit circuit.
circuit = to_qiskit(cirq_circuit)
# Transpile the circuit so it can be properly run
exec_circuit = qiskit.transpile(
circuit,
backend=backend,
basis_gates=noise_model.basis_gates if noise_model else None,
optimization_level=0, # Important to preserve folded gates.
)
# Run the circuit
job = backend.run(exec_circuit, shots=shots)
# 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’s `zne.execute_with_zne()`

function as follows.

```
unmitigated = cirq_ibm_executor(cirq_circuit)
mitigated = zne.execute_with_zne(cirq_circuit, cirq_ibm_executor)
print(f"Unmitigated result {unmitigated:.3f}")
print(f"Mitigated result {mitigated:.3f}")
```

```
Unmitigated result 0.918
Mitigated result 0.957
```

As long as a circuit and a function for executing the circuit are defined, the `zne.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 `zne.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 = zne.inference.LinearFactory(scale_factors=[1.0, 1.5, 2.0, 2.5, 3.0])
mitigated = zne.execute_with_zne(cirq_circuit, cirq_ibm_executor, factory=linear_factory)
print(f"Mitigated result {mitigated:.3f}")
```

```
Mitigated result 0.987
```

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 global folding (instead of local folding, the default behavior for
`zne.execute_with_zne()`

).

```
mitigated = zne.execute_with_zne(cirq_circuit, cirq_ibm_executor, scale_noise=zne.scaling.fold_global)
print(f"Mitigated result {mitigated:.3f}")
```

```
Mitigated result 0.976
```

Any different combination of noise scaling and extrapolation technique can be passed as arguments to
`zne.execute_with_zne()`

.

## Lower-level usage#

Here, we give more detailed usage of the Mitiq library which mimics what happens in the call to
`zne.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.

```
scale_factors = [1., 1.5, 2., 2.5, 3.]
folded_circuits = [
zne.scaling.fold_gates_at_random(to_qiskit(cirq_circuit), scale)
for scale in scale_factors
]
# Check that the circuit depth is (approximately) scaled as expected
for j, c in enumerate(folded_circuits):
print(f"Number of gates of folded circuit {j} scaled by: {len(c) / len(cirq_circuit):.3f}")
```

```
Number of gates of folded circuit 0 scaled by: 1.000
Number of gates of folded circuit 1 scaled by: 1.364
Number of gates of folded circuit 2 scaled by: 1.909
Number of gates of folded circuit 3 scaled by: 2.273
Number of gates of folded circuit 4 scaled by: 2.818
```

For a noiseless simulation, the expectation of this observable should be 1.0 because our circuit compiles to the identity. For a 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.

Below we execute the folded circuits using the `backend`

defined at the start of this example.

```
shots = 8192
# Transpile the circuit so it can be properly run
exec_circuit = qiskit.transpile(
folded_circuits,
backend=backend,
basis_gates=noise_model.basis_gates if noise_model else None,
optimization_level=0, # Important to preserve folded gates.
)
# Run the circuit
job = backend.run(exec_circuit, 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]
print(f"Expectation values:\n{expectation_values}")
```

```
Expectation values:
[0.908447265625, 0.8720703125, 0.8287353515625, 0.80322265625, 0.7657470703125]
```

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: 0.908
```

Now we can use the static `extrapolate`

method of `zne.inference.Factory`

objects to extrapolate to the zero-noise limit. Below we use an exponential fit and print out the extrapolated zero-noise value.

```
zero_noise_value = zne.ExpFactory.extrapolate(scale_factors, expectation_values, asymptote=0.5)
print(f"Extrapolated zero-noise value:", round(zero_noise_value, 3))
```

```
Extrapolated zero-noise value: 1.007
```