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# Error mitigation with Cirq on IBMQ backends
This tutorial shows an example of how to mitigate noise on IBMQ backends with the [Cirq frontend](https://quantumai.google/cirq).
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, broken down in the following steps.
- [](#settings)
- [](#setup-defining-a-circuit-in-cirq)
- [](#high-level-usage)
- [](#options)
- [](#lower-level-usage)
+++
## Settings
```{code-cell} ipython3
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.
(examples/cirq-ibmq-backends/setup-defining-a-circuit)=
## 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.
```{code-cell} ipython3
import cirq
qbit = cirq.LineQubit(0)
cirq_circuit = cirq.Circuit([cirq.X(qbit)] * 10, cirq.measure(qbit))
print(cirq_circuit)
```
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:
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.
For information on how to define more advanced executors, see the {ref}`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
for instructions to create an account, save credentials, and see online quantum computers.
```{code-cell} ipython3
import qiskit
from qiskit_aer import Aer
from qiskit_ibm_provider import IBMProvider
from mitiq.interface.mitiq_qiskit.conversions import to_qiskit
if IBMProvider.saved_accounts() and USE_REAL_HARDWARE:
provider = IBMProvider()
backend = provider.get_backend("ibmq_qasm_simulator") # Set quantum computer here!
else:
# Default to a simulator.
backend = Aer.get_backend("qasm_simulator"),
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)
if USE_REAL_HARDWARE:
# Run the circuit on hardware
job = qiskit.execute(
experiments=circuit,
backend=backend,
optimization_level=0, # Important to preserve folded gates.
shots=shots
)
else:
# Simulate the circuit with noise
noise_model = initialized_depolarizing_noise(noise_level=0.02)
job = qiskit.execute(
experiments=circuit,
backend=qiskit.Aer.get_backend("qasm_simulator"),
noise_model=noise_model,
basis_gates=noise_model.basis_gates,
optimization_level=0, # Important to preserve folded gates.
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 {func}`.zne.execute_with_zne` function as follows.
```{code-cell} ipython3
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}")
```
As long as a circuit and a function for executing the circuit are defined, the {func}`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 {func}`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 {class}`.Factory` object to {func}`execute_with_zne`. The
following code block shows an example of using linear extrapolation with five different (noise) scale factors.
```{code-cell} ipython3
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}")
```
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
{func}`zne.execute_with_zne`).
```{code-cell} ipython3
mitigated = zne.execute_with_zne(cirq_circuit, cirq_ibm_executor, scale_noise=zne.scaling.fold_global)
print(f"Mitigated result {mitigated:.3f}")
```
Any different combination of noise scaling and extrapolation technique can be passed as arguments to
{func}`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
{func}`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.
```{code-cell} ipython3
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}")
```
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.
```{code-cell} ipython3
shots = 8192
if USE_REAL_HARDWARE:
# Run the circuit on hardware
job = qiskit.execute(
experiments=folded_circuits,
backend=backend,
optimization_level=0, # Important to preserve folded gates.
shots=shots
)
else:
# Simulate the circuit with noise
noise_model = initialized_depolarizing_noise(noise_level=0.05)
job = qiskit.execute(
experiments=folded_circuits,
backend=qiskit.Aer.get_backend("qasm_simulator"),
noise_model=noise_model,
basis_gates=noise_model.basis_gates,
optimization_level=0, # Important to preserve folded gates.
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.
```{code-cell} ipython3
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}")
```
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.)
```{code-cell} ipython3
print("Unmitigated expectation value:", round(expectation_values[0], 3))
```
Now we can use the static ``extrapolate`` method of {class}`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.
```{code-cell} ipython3
zero_noise_value = zne.ExpFactory.extrapolate(scale_factors, expectation_values, asymptote=0.5)
print(f"Extrapolated zero-noise value:", round(zero_noise_value, 3))
```