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

Cirq comes with built-in Python simulators for testing small circuits. The two main types of simulations that Cirq supports are pure state and mixed state. The pure state simulations are supported by cirq.Simulator and the mixed state simulators are supported by cirq.DensityMatrixSimulator.

The names pure state simulator and mixed state simulators refer to the fact that these simulations are for quantum circuits; including unitary, measurements, and noise that keeps the evolution in a pure state (i.e. a single quantum state) or a mixed state (a mix of quantum states, each with a classical probability). Noisy evolutions are supported by the pure state simulator, as long as they preserve the purity of the state. If you are interested in truly noisy simulation that may not preserve purity, see the Noisy Simulation page.

Some external high-performance simulators also provide an interface to Cirq. These can often provide results faster than Cirq's built-in simulators, especially when working with larger circuits. For details on these tools, see the external simulators section.

Introduction to pure state simulation

Here is a simple circuit:

q0 = cirq.GridQubit(0, 0)
q1 = cirq.GridQubit(1, 0)

def basic_circuit(meas=True):
    sqrt_x = cirq.X**0.5
    yield sqrt_x(q0), sqrt_x(q1)
    yield cirq.CZ(q0, q1)
    yield sqrt_x(q0), sqrt_x(q1)
    if meas:
        yield cirq.measure(q0, key='q0'), cirq.measure(q1, key='q1')

circuit = cirq.Circuit()

(0, 0): ───X^0.5───@───X^0.5───M('q0')───
(1, 0): ───X^0.5───@───X^0.5───M('q1')───

You can simulate this by creating a cirq.Simulator and passing the circuit into its run method:

simulator = cirq.Simulator()
result =


The method run() returns a Result. As you can see, the object result contains the result of any measurements for the simulation run.

The actual measurement results depend on the random seed generator (numpy). You can set this seed by passing an integer or numpy.RandomState as the seed parameter in the Simulator constructor.

Another run can result in a different set of measurement results:

result =


The run() methods (run() and run_sweep()) are designed to mimic what running a program on a quantum computer is actually like. result only contains measurement data, and the complete state vector is hidden.

Accessing the state vector

To access the full state vector, the simulate() methods (simulate(), simulate_sweep(), simulate_moment_steps()) can be used instead. This behavior is only possible in simulation, but can be useful for debugging a circuit:

import numpy as np

circuit = cirq.Circuit()
result = simulator.simulate(circuit, qubit_order=[q0, q1])

print(np.around(result.final_state_vector, 3))
[0.5+0.j  0. +0.5j 0. +0.5j 0.5+0.j ]

simulate() returns a SimulationTrialResult containing the final state, as seen above. The built-in Cirq simulator returns a StateVectorTrialResult , which includes a number of utilities for analyzing the final state vector.

Note that the simulator uses numpy's float32 precision (which is complex64 for complex numbers) by default, but that the simulator can take in a dtype of np.complex128 if higher precision is needed.

Expectation values

For applications that measure expectation values of observables, the simulate_expectation_values() method provides a simple interface for returning just the desired expectation values. This can be more efficient than returning the entire state vector, particularly when handling multiple results at once, or when using an external simulator.

XX_obs = cirq.X(q0) * cirq.X(q1)
ZZ_obs = cirq.Z(q0) * cirq.Z(q1)
ev_list = simulator.simulate_expectation_values(
    cirq.Circuit(basic_circuit(False)), observables=[XX_obs, ZZ_obs]
[(1+0j), 0j]

simulate_expectation_values() returns a list of expectation values, one for each observable provided.

Qubit and Amplitude Ordering

The qubit_order argument to the simulator's run() method determines the ordering of some results, such as the amplitudes in the final wave function. The qubit_order argument is optional: when it is omitted, qubits are ordered ascending by their name (i.e., what str(qubit) returns).

The simplest qubit_order value you can provide is a list of the qubits in the desired order. Any qubits from the circuit that are not in the list will be ordered using the default str(qubit) ordering, but come after qubits that are in the list. Be aware that all qubits in the list are included in the simulation, even if they are not operated on by the circuit.

The mapping from the order of the qubits to the order of the amplitudes in the wave function can be tricky to understand. Basically, it is the same as the ordering used by numpy.kron:

outside = [1, 10]
inside = [1, 2]
print(np.kron(outside, inside))
[ 1  2 10 20]

More concretely, the k'th amplitude in the wave function will correspond to the k'th case that would be encountered when nesting loops over the possible values of each qubit.

The first qubit's computational basis values are looped over in the outermost loop, the last qubit's computational basis values are looped over in the inner-most loop, etc.:

i = 0
for first in [0, 1]:
    for second in [0, 1]:
        print('amps[{}] is for first={}, second={}'.format(i, first, second))
        i += 1
amps[0] is for first=0, second=0
amps[1] is for first=0, second=1
amps[2] is for first=1, second=0
amps[3] is for first=1, second=1

You can check that this is in fact the ordering with a circuit that flips one qubit out of two:

q_stay = cirq.NamedQubit('q_stay')
q_flip = cirq.NamedQubit('q_flip')
c = cirq.Circuit(cirq.X(q_flip))

# first qubit in order flipped
result = simulator.simulate(c, qubit_order=[q_flip, q_stay])
[0. 0. 1. 0.]
# second qubit in order flipped
result = simulator.simulate(c, qubit_order=[q_stay, q_flip])
[0. 1. 0. 0.]

Stepping through a circuit

When debugging, it is useful to not just see the end result of a circuit, but to inspect the state of the system at different steps in the circuit.

To support this, Cirq provides a method to return an iterator over a Moment by Moment simulation. This method is named simulate_moment_steps:

circuit = cirq.Circuit()
for i, step in enumerate(simulator.simulate_moment_steps(circuit)):
    print('state at step %d: %s' % (i, np.around(step.state_vector(copy=True), 3)))
state at step 0: [0. +0.5j 0.5+0.j  0.5+0.j  0. -0.5j]
state at step 1: [0. +0.5j 0.5+0.j  0.5+0.j  0. +0.5j]
state at step 2: [0.5+0.j  0. +0.5j 0. +0.5j 0.5+0.j ]
state at step 3: [0.+0.j 0.+0.j 0.+1.j 0.+0.j]

The object returned by the moment_steps iterator is a StepResult. This object has the state along with any measurements that occurred before or during that step.

Alternate stepping behavior

For simulators that do not support simulate_moment_steps, it is possible to replicate this behavior by splitting the circuit into "chunks" and passing the results of each chunk as the initial state for the next chunk:

chunks = [cirq.Circuit(moment) for moment in basic_circuit()]
next_state = 0  # represents the all-zero state
for i, chunk in enumerate(chunks):
    result = simulator.simulate(chunk, initial_state=next_state)
    next_state = result.final_state_vector
    print(f'state at step {i}: {np.around(next_state, 3)}')
state at step 0: [0. +0.5j 0.5+0.j  0.5+0.j  0. -0.5j]
state at step 1: [0. +0.5j 0.5+0.j  0.5+0.j  0. +0.5j]
state at step 2: [0.5+0.j  0. +0.5j 0. +0.5j 0.5+0.j ]
state at step 3: [0.+0.j 0.+0.j 0.+1.j 0.+0.j]

The added cost of passing state vectors around like this is nontrivial; for this reason, this workaround should only be used with simulators that do not support simulate_moment_steps.

Parameterized values and studies

In addition to circuit gates with fixed values, Cirq also supports gates which can have Symbol values (see Gates). These are values that can be resolved at runtime.

For simulators, these values are resolved by providing a cirq.ParamResolver. A cirq.ParamResolver provides a map from the Symbol's name to its assigned value.

import sympy

rot_w_gate = cirq.X ** sympy.Symbol('x')
circuit = cirq.Circuit()
circuit.append([rot_w_gate(q0), rot_w_gate(q1)])
for y in range(5):
    resolver = cirq.ParamResolver({'x': y / 4.0})
    result = simulator.simulate(circuit, resolver)
    print(f"params:{result.params}, state vector:{np.round(result.final_state_vector, 2)}")
(0, 0): ───X^x───

(1, 0): ───X^x───
params:cirq.ParamResolver({'x': 0.0}), state vector:[1.+0.j 0.+0.j 0.+0.j 0.+0.j]
params:cirq.ParamResolver({'x': 0.25}), state vector:[ 0.6 +0.6j   0.25-0.25j  0.25-0.25j -0.1 -0.1j ]
params:cirq.ParamResolver({'x': 0.5}), state vector:[0. +0.5j 0.5+0.j  0.5+0.j  0. -0.5j]
params:cirq.ParamResolver({'x': 0.75}), state vector:[-0.1 +0.1j   0.25+0.25j  0.25+0.25j  0.6 -0.6j ]
params:cirq.ParamResolver({'x': 1.0}), state vector:[0.+0.j 0.+0.j 0.+0.j 1.+0.j]

In the previous example, the symbol x is used in two gates, and then the resolver provides this value at run time.

Parameterized values are most useful in defining what is called a "sweep", which is a sequence of trials, where each trial is a run with a particular set of parameter values.

Running a sweep returns a Result for each set of fixed parameter values and repetitions.

For instance:

resolvers = [cirq.ParamResolver({'x': y / 2.0}) for y in range(3)]
circuit = cirq.Circuit()
circuit.append([rot_w_gate(q0), rot_w_gate(q1)])
circuit.append([cirq.measure(q0, key='q0'), cirq.measure(q1, key='q1')])
results = simulator.run_sweep(program=circuit, params=resolvers, repetitions=2)
for result in results:
params:cirq.ParamResolver({'x': 0.0})
params:cirq.ParamResolver({'x': 0.5})
params:cirq.ParamResolver({'x': 1.0})

The previous example demonstrates that assigning different values to gate parameters yields different results for each trial in the sweep, and that each trial is repeated repetitions times. See Parameter Sweeps for more information on sweeping and parameters.

Mixed state simulations

In addition to pure state simulation, Cirq also supports simulation of mixed states.

Even though this simulator is not as efficient as the pure state simulators, they allow for a larger class of noisy circuits to be run as well as keeping track of the simulation's density matrix. This fact can allow for more exact simulations: the density matrix can represent all possible results of a noisy circuit, while the pure-state simulator can only sample from these results.

Mixed state simulation is supported by the cirq.DensityMatrixSimulator class.

Here is a simple example of simulating a channel using the mixed state simulator:

q = cirq.NamedQubit('a')
circuit = cirq.Circuit(cirq.H(q), cirq.amplitude_damp(0.2)(q), cirq.measure(q))
simulator = cirq.DensityMatrixSimulator()
result =, repetitions=100)
Counter({1: 50, 0: 50})

The previous example creates a state in an equal superposition of 0 and 1, then applies amplitude damping which takes 1 to 0 with something like a probability of 0.2.

You can see that, instead of about 50 percent of the timing being in 0, about 20 percent of the 1 has been converted into 0, so you end up with total around 60 percent in the 0 state.

Like the pure state simulators, the mixed state simulator supports run() and run_sweeps() methods.

The cirq.DensityMatrixSimulator also supports getting access to the density matrix of the circuit at the end of simulating the circuit, or when stepping through the circuit. These are done by the simulate() and simulate_sweep() methods, or, for stepping through the circuit, via the simulate_moment_steps method. For example, you can simulate creating an equal superposition followed by an amplitude damping channel with a gamma of 0.2 by:

q = cirq.NamedQubit('a')
circuit = cirq.Circuit(cirq.H(q), cirq.amplitude_damp(0.2)(q))
simulator = cirq.DensityMatrixSimulator()
result = simulator.simulate(circuit)
print(np.around(result.final_density_matrix, 3))
[[0.6  +0.j 0.447+0.j]
 [0.447+0.j 0.4  +0.j]]

External simulators

There are a few high-performance circuit simulators which provide an interface for simulating Cirq Circuits. These projects are listed below, along with their PyPI package name and a description of simulator methods that they support.

For most users we recommend qsim, but each simulator is optimized for specific use cases. Before choosing a simulator, make sure it supports the behavior that you need!

Project name PyPI package Description
qsim qsimcirq Implements cirq.SimulatesAmplitudes, cirq.SimulatesFinalState, and cirq.SimulatesExpectationValues. Recommended for deep circuits with up to 30 qubits (consumes 8GB RAM). Larger circuits are possible, but RAM usage doubles with each additional qubit.
qsimh qsimcirq Implements cirq.SimulatesAmplitudes. Intended for heavy parallelization across several computers; Cirq users should generally prefer qsim.
quimb quimb Not based on cirq.Simulator; instead uses a Cirq-to-quimb translation layer provided in contrib/quimb. In addition to circuit simulation, this allows the use of quimb circuit-analysis tools on Cirq circuits.
qFlex qflexcirq Implements cirq.SimulatesAmplitudes. RAM usage is highly dependent on the number of two-qubit gates in the circuit. Not recommended - prefer qsim or quimb.