# Representing noise

try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq --pre
print("installed cirq.")
import cirq


Cirq provides several built-in tools for representing noise at multiple levels:

This doc describes these options and the types of real-world noise they can be used to represent.

## Channels

Errors in hardware can be broadly separated into two categories: coherent and incoherent.

Coherent errors apply a reversible (but unknown) transformation, such as making every $$Z$$ gate instead behave as $$Z^{1.01}$$. This can be represented by inserting gates into the intended circuit.

Incoherent errors cause decoherence of the quantum state, and are irreversible as a result. This is equivalent to applying an operation with some probability $$0 < P < 1$$, and can be represented with Cirq "channels". ops/common_channels.py defines channels for some of the most common incoherent errors, which are described below.

### Bit flip

cirq.BitFlipChannel (or cirq.bit_flip) is equivalent to applying cirq.X with a given probability. This channel is best used to represent state-agnostic bit flip errors in the body of a circuit.

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.bit_flip(p=0.2).on(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print(result.histogram(key='result'))

Counter({0: 795, 1: 205})


For bit flips which depend on the qubit state, see Amplitude damping.

For measurement error that doesn't affect the quantum state, see Invert mask.

### Amplitude damping

cirq.AmplitudeDampingChannel (or cirq.amplitude_damp) performs a $$|1\rangle \rightarrow |0\rangle$$ transformation with some probability gamma, leaving the existing $$|0\rangle$$ state alone. This channel is best used to represent an idealized form of energy dissipation, where qubits decay from $$|1\rangle$$ to $$|0\rangle$$.

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.X(q0),
cirq.amplitude_damp(gamma=0.2).on(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print(result.histogram(key='result'))

Counter({1: 795, 0: 205})


For state-agnostic bit flips, see Bit flip.

### Generalized amplitude damping

cirq.GeneralizedAmplitudeDampingChannel (or cirq.generalized_amplitude_damp) is a generalized version of AmplitudeDampingChannel. It represent a more realistic bidirectional energy dissipation, in which qubits experience not only decay but also spontaneous excitation. In this channel, gamma represents the probability of energy transfer (excitation OR decay) and a new parameter p gives the probability that the environment is excited.

This is equivalent to excitation with probability (1-p) * gamma and decay with probability p * gamma.

q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
cirq.X(q1),
cirq.generalized_amplitude_damp(gamma=0.2, p=0.2).on_each(q0, q1),
cirq.measure(q0, key='result_0'),
cirq.measure(q1, key='result_1'),
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print("Starting in |0):", result.histogram(key='result_0'))
print("Starting in |1):", result.histogram(key='result_1'))

Starting in |0): Counter({0: 824, 1: 176})
Starting in |1): Counter({1: 956, 0: 44})


### Phase flip or damping

cirq.PhaseFlipChannel (or cirq.phase_flip) is equivalent to applying cirq.Z with a given probability p. This channel is best used to represent state-agnostic phase flip errors in the body of a circuit.

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.H(q0),
cirq.phase_flip(p=0.2).on(q0),
cirq.H(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print("Phase flip:", result.histogram(key='result'))

Phase flip: Counter({0: 795, 1: 205})


cirq.PhaseDampingChannel (or cirq.phase_damp) is a different way of expressing the same behavior: for any given value of p, PhaseFlipChannel(p=p) is equivalent to PhaseDampingChannel(gamma=(1-(2*p-1)**2)).

q0 = cirq.LineQubit(0)
# Convert p=0.2 to gamma
p = 0.2
gamma = 1 - (2 * p - 1) ** 2
print(f"{gamma=}")
circuit = cirq.Circuit(
cirq.H(q0),
cirq.phase_damp(gamma=gamma).on(q0),
cirq.H(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print("Phase damp:", result.histogram(key='result'))

gamma=0.64
Phase damp: Counter({0: 777, 1: 223})


Note that the results differ despite the same seed and equivalent circuits. This is due to the channels having different operators, which interact differently with Cirq's RNG.

### Depolarization

cirq.DepolarizingChannel (or cirq.depolarize) is equivalent to applying a randomly-selected Pauli operator to the target qubits. The identity is applied with probability 1-p; all other Pauli operators have an equal probability p / (4**n-1) of being selected. This channel is best used for representing uniformly-distributed decoherence of the target qubit(s) across all Pauli channels.

q0, q1, q2 = cirq.LineQubit.range(3)
circuit = cirq.Circuit(
cirq.H(q0),  # initialize X basis
cirq.H(q1),  # initialize Y basis
cirq.S(q1),
cirq.depolarize(p=0.2).on_each(q0, q1, q2),
cirq.S(q1) ** -1,
cirq.H(q1),
cirq.measure(q0, key='result_0'),
cirq.measure(q1, key='result_1'),
cirq.measure(q2, key='result_2'),
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
# All basis states are equally affected.
print("X basis:", result.histogram(key='result_0'))
print("Y basis:", result.histogram(key='result_1'))
print("Z basis:", result.histogram(key='result_2'))

X basis: Counter({0: 872, 1: 128})
Y basis: Counter({0: 862, 1: 138})
Z basis: Counter({0: 892, 1: 108})


For noise in just the X or Z channels, see Bit flip or Phase flip respectively.

### Asymmetric depolarization

cirq.AsymmetricDepolarizingChannel (or cirq.asymmetric_depolarize) is a generalized version of DepolarizingChannel which accepts separate probabilities for X, Y, and Z error. It is best used instead of DepolarizingChannel when there is a known, nontrivial discrepancy between the different Pauli error modes.

q0, q1, q2 = cirq.LineQubit.range(3)
asym_depol = cirq.asymmetric_depolarize(p_x=0, p_y=0.05, p_z=0.2)
circuit = cirq.Circuit(
cirq.H(q0),  # initialize X basis
cirq.H(q1),  # initialize Y basis
cirq.S(q1),
asym_depol.on_each(q0, q1, q2),
cirq.S(q1) ** -1,
cirq.H(q1),
cirq.measure(q0, key='result_0'),
cirq.measure(q1, key='result_1'),
cirq.measure(q2, key='result_2'),
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
# Basis states are only affected by error in other bases.
print("X basis:", result.histogram(key='result_0'))
print("Y basis:", result.histogram(key='result_1'))
print("Z basis:", result.histogram(key='result_2'))

X basis: Counter({0: 764, 1: 236})
Y basis: Counter({0: 800, 1: 200})
Z basis: Counter({0: 950, 1: 50})


### Reset

cirq.Reset forces a qubit into the $$|0\rangle$$ state. This is not a noise channel, but rather a hardware operation which commonly consists of measuring the qubit and applying X as needed to return it to the $$|0\rangle$$ state.

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.bit_flip(p=0.2).on(q0),
cirq.reset(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print(result.histogram(key='result'))

Counter({0: 1000})


### Custom channels

cirq.MixedUnitaryChannel (in ops/mixed_unitary_channel.py) is a customizable channel which can represent any probabilistic mixture of unitary operators. It accepts an optional measurement key to capture which operator was selected.

q0 = cirq.LineQubit(0)
# equivalent to cirq.bit_flip(p=0.2)
my_channel = cirq.MixedUnitaryChannel(
[(0.8, cirq.unitary(cirq.I)), (0.2, cirq.unitary(cirq.X))],
key='op_num',
)
circuit = cirq.Circuit(
my_channel.on(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=20)
# op_num and result are always equal.
print(result)

op_num=00001000001000000001
result=00001000001000000001


cirq.KrausChannel (in ops/kraus_channel.py) is similar, but supports non-unitary operators.

import numpy as np

q0 = cirq.LineQubit(0)
# equivalent to cirq.amplitude_damp(gamma=0.2)
gamma = 0.2
my_channel = cirq.KrausChannel(
[
np.array([[0, np.sqrt(gamma)], [0, 0]]),    # decay |1) -> |0)
np.array([[1, 0], [0, np.sqrt(1-gamma)]]),  # stay in |1)
],
key='op_num',
)
circuit = cirq.Circuit(
cirq.X(q0),
my_channel.on(q0),
cirq.measure(q0, key='result')
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=20)
# op_num and result are always equal.
print(result)

op_num=11111110011100111011
result=11111110011100111011


In general, prefer one of the other built-in channels if your use case supports it, as those channels can occasionally be optimized in ways that do not generalize to these channels.

Prefer MixedUnitaryChannel if your channel has a mix-of-unitaries description, as it can be simulated more efficiently than KrausChannel.

## NoiseModels

Built-in cirq.NoiseModel types do not have a shared home like channels, but a couple of commonly-used types are listed here. For more complex experiments, it is often useful to define your own NoiseModel subclasses; refer to devices/noise_model.py to learn more.

### Constant noise

cirq.ConstantQubitNoiseModel (in devices/noise_model.py) is a simple model which will insert the given gate after every operation in the target circuit. When "trivially converting" gates to NoiseModels, this is the model that is used, but it isn't particularly representative of any real-world noise.

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.I(q0),
cirq.measure(q0, key='result_0'),
cirq.measure(q0, key='result_1'),
)
# Applies noise after every gate, even measurements.
noisy_circuit = circuit.with_noise(cirq.X)
print(noisy_circuit)
result = cirq.Simulator(seed=0).run(noisy_circuit, repetitions=20)
print("First measure:", result.histogram(key='result_0'))
print("Second measure:", result.histogram(key='result_1'))

0: ───I───X[cirq.VirtualTag()]───M('result_0')───X[cirq.VirtualTag()]───M('result_1')───X[cirq.VirtualTag()]───
First measure: Counter({1: 20})
Second measure: Counter({0: 20})


Avoid using this model except for simple tests, as different gates (particulary cirq.MeasurementGate) usually have different error.

### Insertion noise

cirq.devices.InsertionNoiseModel (in devices/insertion_noise_model.py) inspects the circuit for operations matching user-specified identifiers, and inserts the corresponding noise operations after matching operations. This noise model is useful for applying specific noise to specific gates - for example, adding different depolarizing error to 1- and 2-qubit gates.

from cirq.devices import InsertionNoiseModel

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.I(q0),
cirq.X(q0),
cirq.measure(q0, key='result'),
)
# Apply bitflip noise after each X gate.
target_op = cirq.OpIdentifier(cirq.XPowGate, q0)
insert_op = cirq.bit_flip(p=0.2).on(q0)
noise_model = InsertionNoiseModel(
require_physical_tag=False,  # For use outside calibration-to-noise
)
noisy_circuit = circuit.with_noise(noise_model)
print(noisy_circuit)
result = cirq.Simulator(seed=0).run(noisy_circuit, repetitions=1000)
print(result.histogram(key='result'))

0: ───I───X───BF(0.2)───M('result')───
Counter({1: 795, 0: 205})


InsertionNoiseModel is primarily used in the calibration-to-noise pipeline, but can be used elsewhere by setting require_physical_tag=False, as seen above.

## Measurement parameters

cirq.MeasurementGate provides parameters for error which occurs in the classical measurement step instead of in the quantum state, which can be useful for accelerating simulations.

The invert_mask field is a simple list of booleans indicating bits to flip in the final output. This can represent simple bitflip error in measurement, or a correction for that error.

q0 = cirq.LineQubit(0)
circuit = cirq.Circuit(
cirq.X(q0),
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print(result.histogram(key='result'))

Counter({0: 1000})


### Confusion map

The confusion_map field maps qubit tuples to confusion matrices for those qubits. The confusion matrix for two qubits is:

$\begin{bmatrix} Pr(00|00) & Pr(01|00) & Pr(10|00) & Pr(11|00) \\ Pr(00|01) & Pr(01|01) & Pr(10|01) & Pr(11|01) \\ Pr(00|10) & Pr(01|10) & Pr(10|10) & Pr(11|10) \\ Pr(00|11) & Pr(01|11) & Pr(10|11) & Pr(11|11) \end{bmatrix}$

where Pr(ij|pq) is the probability of observing ij if state pq was prepared; a 2**n-square confusion matrix can be provided for any grouping of N qubits.

import numpy as np

q0 = cirq.LineQubit(0)
# 10% chance to report |0) as |1), 20% chance to report |1) as |0).
cmap = {(0,): np.array([[0.9, 0.1], [0.2, 0.8]])}
circuit = cirq.Circuit(
cirq.X(q0),
cirq.measure(q0, key='result', confusion_map=cmap)
)
result = cirq.Simulator(seed=0).run(circuit, repetitions=1000)
print(result.histogram(key='result'))

Counter({1: 787, 0: 213})


This can be used for representing more complex errors in measurement, including probabilistic error on individual qubits and correlated error across multiple qubits.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]