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.
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.
Samples the given Circuit, producing a pandas data frame.
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)) out0 11 12 13 1
Calculates estimated expectation values from samples of a circuit.
Please see also cirq.work.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.
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.
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 TActOnArgs. 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.
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 OperationTarget. 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.