cirq.CliffordSimulator

An efficient simulator for Clifford circuits.

Inherits From: SimulatorBase, SimulatesIntermediateState, SimulatesFinalState, SimulatesSamples, Sampler

cirq.CliffordSimulator(
    seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
    split_untangled_states: bool = False
)

Used in the notebooks

Used in the tutorials
Hidden linear function problem

Args
`seed`	The random seed to use for this simulator.
`split_untangled_states`	Optimizes simulation by running separable states independently and merging those states at the end.

Attributes
`noise`

Attributes

noise

Methods

`is_supported_operation`

View source

@staticmethod
is_supported_operation(
    op: 'cirq.Operation'
) -> bool

Checks whether given operation can be simulated by this simulator.

`run`

View source

run(
    program: 'cirq.AbstractCircuit',
    param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
    repetitions: int = 1
) -> 'cirq.Result'

Samples from the given Circuit.

This mode of operation for a sampler will provide results in the form of measurement outcomes. It will not provide access to state vectors (even if the underlying sampling mechanism is a simulator). This method will substitute parameters in the param_resolver attributes for sympy.Symbols used within the Circuit. This circuit will be executed a number of times specified in the repetitions attribute, though some simulated implementations may instead sample from the final distribution rather than execute the circuit each time.

Args
`program`	The circuit to sample from.
`param_resolver`	Parameters to run with the program.
`repetitions`	The number of times to sample.

Returns
`cirq.Result` that contains all the measurements for a run.

`run_async`

View source

run_async(
    program, param_resolver=None, repetitions=1
)

Asynchronously samples from the given Circuit.

Provides measurement outcomes as a cirq.Result object. This interface will operate in a similar way to the run method except for executing asynchronously.

Args
`program`	The circuit to sample from.
`param_resolver`	Parameters to run with the program.
`repetitions`	The number of times to sample.

Returns
Result for a run.

`run_batch`

View source

run_batch(
    programs, params_list=None, repetitions=1
)

Runs the supplied circuits asynchronously.

Each circuit provided in programs will pair with the optional associated parameter sweep provided in the params_list, and be run with the associated repetitions provided in repetitions (if repetitions is an integer, then all runs will have that number of repetitions). If params_list is specified, then the number of circuits is required to match the number of sweeps. Similarly, when repetitions is a list, the number of circuits is required to match the length of this list.

By default, this method simply invokes run_sweep sequentially for each (circuit, parameter sweep, repetitions) tuple. Child classes that are capable of sampling batches more efficiently should override it to use other strategies. Note that child classes may have certain requirements that must be met in order for a speedup to be possible, such as a constant number of repetitions being used for all circuits. Refer to the documentation of the child class for any such requirements.

Args
`programs`	The circuits to execute as a batch.
`params_list`	Parameter sweeps to use with the circuits. The number of sweeps should match the number of circuits and will be paired in order with the circuits.
`repetitions`	Number of circuit repetitions to run. Can be specified as a single value to use for all runs, or as a list of values, one for each circuit.

Returns
A list of lists of TrialResults. The outer list corresponds to the circuits, while each inner list contains the TrialResults for the corresponding circuit, in the order imposed by the associated parameter sweep.

Raises
`ValueError`	If length of `programs` is not equal to the length of `params_list` or the length of `repetitions`.

`run_batch_async`

View source

run_batch_async(
    programs, params_list=None, repetitions=1
)

Runs the supplied circuits asynchronously.

Args
`programs`	The circuits to execute as a batch.
`params_list`	Parameter sweeps to use with the circuits. The number of sweeps should match the number of circuits and will be paired in order with the circuits.
`repetitions`	Number of circuit repetitions to run. Can be specified as a single value to use for all runs, or as a list of values, one for each circuit.

Returns
A list of lists of TrialResults. The outer list corresponds to the circuits, while each inner list contains the TrialResults for the corresponding circuit, in the order imposed by the associated parameter sweep.

Raises
`ValueError`	If length of `programs` is not equal to the length of `params_list` or the length of `repetitions`.

`run_sweep`

View source

run_sweep(
    program: 'cirq.AbstractCircuit',
    params: 'cirq.Sweepable',
    repetitions: int = 1
) -> Sequence['cirq.Result']

Samples from the given Circuit.

This allows for sweeping over different parameter values, unlike the run method. The params argument will provide a mapping from sympy.Symbols used within the circuit to a set of values. Unlike the run method, which specifies a single mapping from symbol to value, this method allows a "sweep" of values. This allows a user to specify execution of a family of related circuits efficiently.

Args
`program`	The circuit to sample from.
`params`	Parameters to run with the program.
`repetitions`	The number of times to sample.

Returns
Result list for this run; one for each possible parameter resolver.

`run_sweep_async`

View source

run_sweep_async(
    program, params, repetitions=1
)

Asynchronously samples from the given Circuit.

By default, this method invokes run_sweep synchronously and simply exposes its result is an awaitable. Child classes that are capable of true asynchronous sampling should override it to use other strategies.

Args
`program`	The circuit to sample from.
`params`	Parameters to run with the program.
`repetitions`	The number of times to sample.

Returns
Result list for this run; one for each possible parameter resolver.

`run_sweep_iter`

View source

run_sweep_iter(
    program: 'cirq.AbstractCircuit',
    params: 'cirq.Sweepable',
    repetitions: int = 1
) -> Iterator['cirq.Result']

Runs the supplied Circuit, mimicking quantum hardware.

In contrast to run, this allows for sweeping over different parameter values.

Args
`program`	The circuit to simulate.
`params`	Parameters to run with the program.
`repetitions`	The number of repetitions to simulate.

Returns
Result list for this run; one for each possible parameter resolver.

Raises
`ValueError`	If the circuit has no measurements.

`sample`

View source

sample(
    program: 'cirq.AbstractCircuit',
    *,
    repetitions: int = 1,
    params: 'cirq.Sweepable' = None
) -> 'pd.DataFrame'

Samples the given Circuit, producing a pandas data frame.

This interface will operate in a similar way to the run method except that it returns a pandas data frame rather than a cirq.Result object.

Args
`program`	The circuit to sample from.
`repetitions`	The number of times to sample the program, for each parameter mapping.
`params`	Maps symbols to one or more values. This argument can be a dictionary, a list of dictionaries, a `cirq.Sweep`, a list of `cirq.Sweep`, etc. The program will be sampled `repetition` times for each mapping. Defaults to a single empty mapping.

Returns

Returns
A `pandas.DataFrame` with a row for each sample, and a column for each measurement key as well as a column for each symbolic parameter. Measurement results are stored as a big endian integer representation with one bit for each measured qubit in the key. See `cirq.big_endian_int_to_bits` and similar functions for how to convert this integer into bits. There is an also index column containing the repetition number, for each parameter assignment.

A pandas.DataFrame with a row for each sample, and a column for each measurement key as well as a column for each symbolic parameter. Measurement results are stored as a big endian integer representation with one bit for each measured qubit in the key. See cirq.big_endian_int_to_bits and similar functions for how to convert this integer into bits. There is an also index column containing the repetition number, for each parameter assignment.

Raises
`ValueError`	If a supplied sweep is invalid.

Examples

Examples
`>>> a, b, c = cirq.LineQubit.range(3) >>> sampler = cirq.Simulator() >>> circuit = cirq.Circuit(cirq.X(a), ... cirq.measure(a, key='out')) >>> print(sampler.sample(circuit, repetitions=4)) out 0 1 1 1 2 1 3 1` `circuit = cirq.Circuit(cirq.X(a),` `cirq.CNOT(a, b),` `cirq.measure(a, b, c, key='out'))` `print(sampler.sample(circuit, repetitions=4))` `out` `0 6` `1 6` `2 6` `3 6` `circuit = cirq.Circuit(cirq.X(a)**sympy.Symbol('t'),` `cirq.measure(a, key='out'))` `print(sampler.sample(` `circuit,` `repetitions=3,` `params=[{'t': 0}, {'t': 1}]))` `t out` `0 0 0` `1 0 0` `2 0 0` `0 1 1` `1 1 1` `2 1 1`

>>> a, b, c = cirq.LineQubit.range(3)
>>> sampler = cirq.Simulator()
>>> circuit = cirq.Circuit(cirq.X(a),
...                        cirq.measure(a, key='out'))
>>> print(sampler.sample(circuit, repetitions=4))
   out
0    1
1    1
2    1
3    1

circuit = cirq.Circuit(cirq.X(a),
                       cirq.CNOT(a, b),
                       cirq.measure(a, b, c, key='out'))
print(sampler.sample(circuit, repetitions=4))
   out
0    6
1    6
2    6
3    6

circuit = cirq.Circuit(cirq.X(a)**sympy.Symbol('t'),
                       cirq.measure(a, key='out'))
print(sampler.sample(
    circuit,
    repetitions=3,
    params=[{'t': 0}, {'t': 1}]))
   t  out
0  0    0
1  0    0
2  0    0
0  1    1
1  1    1
2  1    1

`sample_expectation_values`

View source

sample_expectation_values(
    program: 'cirq.AbstractCircuit',
    observables: Union['cirq.PauliSumLike', List['cirq.PauliSumLike']],
    *,
    num_samples: int,
    params: 'cirq.Sweepable' = None,
    permit_terminal_measurements: bool = False
) -> Sequence[Sequence[float]]

Calculates estimated expectation values from samples of a circuit.

Please see also cirq.work.observable_measurement.measure_observables for more control over how to measure a suite of observables.

This method can be run on any device or simulator that supports circuit sampling. Compare with simulate_expectation_values in simulator.py, which is limited to simulators but provides exact results.

Args
`program`	The circuit which prepares a state from which we sample expectation values.
`observables`	A list of observables for which to calculate expectation values.
`num_samples`	The number of samples to take. Increasing this value increases the statistical accuracy of the estimate.
`params`	Parameters to run with the program.
`permit_terminal_measurements`	If the provided circuit ends in a measurement, this method will generate an error unless this is set to True. This is meant to prevent measurements from ruining expectation value calculations.

Returns
A list of expectation-value lists. The outer index determines the sweep, and the inner index determines the observable. For instance, results[1][3] would select the fourth observable measured in the second sweep.

Raises
`ValueError`	If the number of samples was not positive, if empty observables were supplied, or if the provided circuit has terminal measurements and `permit_terminal_measurements` is true.

`simulate`

View source

simulate(
    program: 'cirq.AbstractCircuit',
    param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
    qubit_order: 'cirq.QubitOrderOrList' = cirq.QubitOrder.DEFAULT,
    initial_state: Any = None
) -> TSimulationTrialResult

Simulates the supplied Circuit.

This method returns a result which allows access to the entire simulator's final state.

Args
`program`	The circuit to simulate.
`param_resolver`	Parameters to run with the program.
`qubit_order`	Determines the canonical ordering of the qubits. This is often used in specifying the initial state, i.e. the ordering of the computational basis states.
`initial_state`	The initial state for the simulation. The form of this state depends on the simulation implementation. See documentation of the implementing class for details.

Returns
SimulationTrialResults for the simulation. Includes the final state.

`simulate_moment_steps`

View source

simulate_moment_steps(
    circuit: 'cirq.AbstractCircuit',
    param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
    qubit_order: 'cirq.QubitOrderOrList' = cirq.QubitOrder.DEFAULT,
    initial_state: Any = None
) -> Iterator[TStepResult]

Returns an iterator of StepResults for each moment simulated.

If the circuit being simulated is empty, a single step result should be returned with the state being set to the initial state.

Args
`circuit`	The Circuit to simulate.
`param_resolver`	A ParamResolver for determining values of Symbols.
`qubit_order`	Determines the canonical ordering of the qubits. This is often used in specifying the initial state, i.e. the ordering of the computational basis states.
`initial_state`	The initial state for the simulation. This can be either a raw state or a `TSimulationState`. The form of the raw state depends on the simulation implementation. See documentation of the implementing class for details.

Returns
Iterator that steps through the simulation, simulating each moment and returning a StepResult for each moment.

`simulate_sweep`

View source

simulate_sweep(
    program: 'cirq.AbstractCircuit',
    params: 'cirq.Sweepable',
    qubit_order: 'cirq.QubitOrderOrList' = cirq.QubitOrder.DEFAULT,
    initial_state: Any = None
) -> List[TSimulationTrialResult]

Wraps computed states in a list.

Prefer overriding simulate_sweep_iter.

`simulate_sweep_iter`

View source

simulate_sweep_iter(
    program: 'cirq.AbstractCircuit',
    params: 'cirq.Sweepable',
    qubit_order: 'cirq.QubitOrderOrList' = cirq.QubitOrder.DEFAULT,
    initial_state: Any = None
) -> Iterator[TSimulationTrialResult]

Simulates the supplied Circuit.

This particular implementation overrides the base implementation such that an unparameterized prefix circuit is simulated and fed into the parameterized suffix circuit.

Args
`program`	The circuit to simulate.
`params`	Parameters to run with the program.
`qubit_order`	Determines the canonical ordering of the qubits. This is often used in specifying the initial state, i.e. the ordering of the computational basis states.
`initial_state`	The initial state for the simulation. This can be either a raw state or an `SimulationStateBase`. The form of the raw state depends on the simulation implementation. See documentation of the implementing class for details.

Returns
List of SimulationTrialResults for this run, one for each possible parameter resolver.

cirq.CliffordSimulator

Used in the notebooks

Args

Attributes

Methods

is_supported_operation

run

run_async

run_batch

run_batch_async

run_sweep

run_sweep_async

run_sweep_iter

sample

sample_expectation_values

simulate

simulate_moment_steps

simulate_sweep

simulate_sweep_iter

`is_supported_operation`

`run`

`run_async`

`run_batch`

`run_batch_async`

`run_sweep`

`run_sweep_async`

`run_sweep_iter`

`sample`

`sample_expectation_values`

`simulate`

`simulate_moment_steps`

`simulate_sweep`

`simulate_sweep_iter`