qsimcirq.QSimSimulator

Simulator that mimics running on quantum hardware.

qsimcirq.QSimSimulator(
    qsim_options: Union[None, Dict, qsimcirq.QSimOptions] = None,
    seed: cirq.RANDOM_STATE_OR_SEED_LIKE = None,
    noise: cirq.NOISE_MODEL_LIKE = None,
    circuit_memoization_size: int = 0
)

Used in the notebooks

Used in the tutorials

Implementors of this interface should implement the _run method.

qsim_options An options dict or QSimOptions object with options to use for all circuits run using this simulator. See the QSimOptions class for details.
seed A random state or seed object, as defined in cirq.value.
noise A cirq.NoiseModel to apply to all circuits simulated with this simulator.
circuit_memoization_size The number of last translated circuits to be memoized from simulation executions, to eliminate translation overhead. Every simulation will perform a linear search through the list of memoized circuits using circuit equality checks, so a large circuit_memoization_size with large circuits will incur a significant runtime overhead. Note that every resolved parameterization results in a separate circuit to be memoized.

ValueError if internal keys 'c', 'i' or 's' are included in 'qsim_options'.

Methods

compute_amplitudes

compute_amplitudes(
    program: 'cirq.AbstractCircuit',
    bitstrings: Sequence[int],
    param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
    qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT
) -> Sequence[complex]

Computes the desired amplitudes.

The initial state is assumed to be the all zeros state.

Args
program The circuit to simulate.
bitstrings The bitstrings whose amplitudes are desired, input as an integer array where each integer is formed from measured qubit values according to qubit_order from most to least significant qubit, i.e. in big-endian ordering. If inputting a binary literal add the prefix 0b or 0B. For example: 0010 can be input as 0b0010, 0B0010, 2, 0x2, etc.
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.

Returns
List of amplitudes.

compute_amplitudes_sweep

compute_amplitudes_sweep(
    program: 'cirq.AbstractCircuit',
    bitstrings: Sequence[int],
    params: 'cirq.Sweepable',
    qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT
) -> Sequence[Sequence[complex]]

Wraps computed amplitudes in a list.

Prefer overriding compute_amplitudes_sweep_iter.

compute_amplitudes_sweep_iter

View source

compute_amplitudes_sweep_iter(
    program: cirq.Circuit,
    bitstrings: Sequence[int],
    params: cirq.Sweepable,
    qubit_order: cirq.QubitOrderOrList = cirq.QubitOrder.DEFAULT
) -> Iterator[Sequence[complex]]

Computes the desired amplitudes using qsim.

The initial state is assumed to be the all zeros state.

Args
program The circuit to simulate.
bitstrings The bitstrings whose amplitudes are desired, input as an string array where each string is formed from measured qubit values according to qubit_order from most to least significant qubit, i.e., in big-endian ordering.
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.

Yields
Amplitudes.

get_seed

View source

get_seed()

run

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

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

run_batch(
    programs: Sequence['cirq.AbstractCircuit'],
    params_list: Optional[Sequence['cirq.Sweepable']] = None,
    repetitions: Union[int, Sequence[int]] = 1
) -> Sequence[Sequence['cirq.Result']]

Runs the supplied circuits.

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

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

Runs the supplied circuits asynchronously.

See docs for cirq.Sampler.run_batch.

run_sweep

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

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

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

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
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

>>> 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

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.

sample_from_amplitudes

sample_from_amplitudes(
    circuit: 'cirq.AbstractCircuit',
    param_resolver: 'cirq.ParamResolver',
    seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE',
    repetitions: int = 1,
    qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT
) -> Dict[int, int]

Uses amplitude simulation to sample from the given circuit.

This implements the algorithm outlined by Bravyi, Gosset, and Liu in https://arxiv.org/abs/2112.08499 to more efficiently calculate samples given an amplitude-based simulator.

Simulators which also implement SimulatesSamples or SimulatesFullState should prefer run() or simulate(), respectively, as this method only accelerates sampling for amplitude-based simulators.

Args
circuit The circuit to simulate.
param_resolver Parameters to run with the program.
seed Random state to use as a seed. This must be provided manually - if the simulator has its own seed, it will not be used unless it is passed as this argument.
repetitions The number of repetitions to simulate.
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.

Returns
A dict of bitstrings sampled from the final state of circuit to the number of occurrences of that bitstring.

Raises
ValueError if 'circuit' has non-unitary elements, as differences in behavior between sampling steps break this algorithm.

simulate

simulate(
    program: 'cirq.AbstractCircuit',
    param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
    qubit_order: 'cirq.QubitOrderOrList' = ops.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_expectation_values

simulate_expectation_values(
    program: 'cirq.AbstractCircuit',
    observables: Union['cirq.PauliSumLike', List['cirq.PauliSumLike']],
    param_resolver: 'cirq.ParamResolverOrSimilarType' = None,
    qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT,
    initial_state: Any = None,
    permit_terminal_measurements: bool = False
) -> List[float]

Simulates the supplied circuit and calculates exact expectation values for the given observables on its final state.

This method has no perfect analogy in hardware. Instead compare with Sampler.sample_expectation_values, which calculates estimated expectation values by sampling multiple times.

Args
program The circuit to simulate.
observables An observable or list of observables.
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.
permit_terminal_measurements If the provided circuit ends with measurement(s), 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 values, with the value at index n corresponding to observables[n] from the input.

Raises
ValueError if 'program' has terminal measurement(s) and 'permit_terminal_measurements' is False.

simulate_expectation_values_sweep

simulate_expectation_values_sweep(
    program: 'cirq.AbstractCircuit',
    observables: Union['cirq.PauliSumLike', List['cirq.PauliSumLike']],
    params: 'cirq.Sweepable',
    qubit_order: 'cirq.QubitOrderOrList' = ops.QubitOrder.DEFAULT,
    initial_state: Any = None,
    permit_terminal_measurements: bool = False
) -> List[List[float]]

Wraps computed expectation values in a list.

Prefer overriding simulate_expectation_values_sweep_iter.

simulate_expectation_values_sweep_iter

View source

simulate_expectation_values_sweep_iter(
    program: cirq.Circuit,
    observables: Union[cirq.PauliSumLike, List[cirq.PauliSumLike]],
    params: cirq.Sweepable,
    qubit_order: cirq.QubitOrderOrList = cirq.QubitOrder.DEFAULT,
    initial_state: Any = None,
    permit_terminal_measurements: bool = False
) -> Iterator[List[float]]

Simulates the supplied circuit and calculates exact expectation values for the given observables on its final state.

This method has no perfect analogy in hardware. Instead compare with Sampler.sample_expectation_values, which calculates estimated expectation values by sampling multiple times.

Args
program The circuit to simulate.
observables An observable or list of observables.
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. The form of this state depends on the simulation implementation. See documentation of the implementing class for details.
permit_terminal_measurements If the provided circuit ends with measurement(s), this method will generate an error unless this is set to True. This is meant to prevent measurements from ruining expectation value calculations.

Yields
Lists of expectation values, with the value at index n corresponding to observables[n] from the input.

Raises
ValueError if 'program' has terminal measurement(s) and 'permit_terminal_measurements' is False. (Note: We cannot test this until Cirq's are_any_measurements_terminal is released.)

simulate_into_1d_array

View source

simulate_into_1d_array(
    program: cirq.AbstractCircuit,
    param_resolver: cirq.ParamResolverOrSimilarType = None,
    qubit_order: cirq.QubitOrderOrList = cirq.ops.QubitOrder.DEFAULT,
    initial_state: Any = None
) -> Tuple[cirq.ParamResolver, np.ndarray, Sequence[int]]

Same as simulate() but returns raw simulation result without wrapping it.

The returned result is not wrapped in a StateVectorTrialResult but can be used
to create a StateVectorTrialResult.

Returns
Tuple of (param resolver, final state, qubit order)

simulate_moment_expectation_values

View source

simulate_moment_expectation_values(
    program: cirq.Circuit,
    indexed_observables: Union[Dict[int, Union[cirq.PauliSumLike, List[cirq.PauliSumLike]]], cirq.
        PauliSumLike, List[cirq.PauliSumLike]],
    param_resolver: cirq.ParamResolver,
    qubit_order: cirq.QubitOrderOrList = cirq.QubitOrder.DEFAULT,
    initial_state: Any = None
) -> List[List[float]]

Calculates expectation values at each moment of a circuit.

Args
program The circuit to simulate.
indexed_observables A map of moment indices to an observable or list of observables to calculate after that moment. As a convenience, users can instead pass in a single observable or observable list to calculate after ALL moments.
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.
permit_terminal_measurements If the provided circuit ends with measurement(s), 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 values for each moment m in the circuit, where value n corresponds to indexed_observables[m][n].

Raises
ValueError if 'program' has terminal measurement(s) and 'permit_terminal_measurements' is False. (Note: We cannot test this until Cirq's are_any_measurements_terminal is released.)

simulate_sweep

simulate_sweep(
    program: 'cirq.AbstractCircuit',
    params: 'cirq.Sweepable',
    qubit_order: 'cirq.QubitOrderOrList' = ops.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.Circuit,
    params: cirq.Sweepable,
    qubit_order: cirq.QubitOrderOrList = cirq.QubitOrder.DEFAULT,
    initial_state: Optional[Union[int, np.ndarray]] = None
) -> Iterator[cirq.StateVectorTrialResult]

Simulates the supplied Circuit.

This method returns a result which allows access to the entire wave function. In contrast to simulate, this allows for sweeping over different parameter values.

Avoid using this method with use_gpu=True in the simulator options; when used with GPU this method must copy state from device to host memory multiple times, which can be very slow. This issue is not present in simulate_expectation_values_sweep.

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 either be an integer representing a pure state (e.g. 11010) or a numpy array containing the full state vector. If none is provided, this is assumed to be the all-zeros state.

Returns
Iterator over SimulationTrialResults for this run, one for each possible parameter resolver.

Raises
TypeError if an invalid initial_state is provided.