def test_get_wavefunction__returns_wavefunction(
client_configuration: QCSClientConfiguration):
qvm_client = QVMClient(client_configuration=client_configuration)
respx.post(
url=client_configuration.profile.applications.pyquil.qvm_url,
json={
"type": "wavefunction",
"compiled-quil": "some-program",
"measurement-noise": (3.14, 1.61, 6.28),
"gate-noise": (1.0, 2.0, 3.0),
"rng-seed": 314,
},
).respond(status_code=200, text="some-wavefunction")
request = GetWavefunctionRequest(
program="some-program",
measurement_noise=(3.14, 1.61, 6.28),
gate_noise=(1.0, 2.0, 3.0),
seed=314,
)
assert qvm_client.get_wavefunction(request) == GetWavefunctionResponse(
wavefunction=b"some-wavefunction")
class WavefunctionSimulator:
def __init__(
self,
*,
gate_noise: Optional[Tuple[float, float, float]] = None,
measurement_noise: Optional[Tuple[float, float, float]] = None,
random_seed: Optional[int] = None,
timeout: float = 10.0,
client_configuration: Optional[QCSClientConfiguration] = None,
) -> None:
"""
A simulator that propagates a wavefunction representation of a quantum state.
:param gate_noise: A tuple of three numbers [Px, Py, Pz] indicating the probability of an X,
Y, or Z gate getting applied to each qubit after a gate application or reset.
:param measurement_noise: A tuple of three numbers [Px, Py, Pz] indicating the probability
of an X, Y, or Z gate getting applied before a measurement.
:param random_seed: A seed for the simulator's random number generators. Either None (for
an automatically generated seed) or a non-negative integer.
:param timeout: Time limit for requests, in seconds.
:param client_configuration: Optional client configuration. If none is provided, a default one will be loaded.
"""
validate_noise_probabilities(gate_noise)
validate_noise_probabilities(measurement_noise)
self.gate_noise = gate_noise
self.measurement_noise = measurement_noise
if random_seed is None:
self.random_seed = None
elif isinstance(random_seed, int) and random_seed >= 0:
self.random_seed = random_seed
else:
raise TypeError("random_seed should be None or a non-negative int")
client_configuration = client_configuration or QCSClientConfiguration.load(
)
self._qvm_client = QVMClient(client_configuration=client_configuration,
request_timeout=timeout)
def wavefunction(
self,
quil_program: Program,
memory_map: Optional[Dict[str, List[Union[int, float]]]] = None
) -> Wavefunction:
"""
Simulate a Quil program and return the wavefunction.
.. note:: If your program contains measurements or noisy gates, this method may not do what
you want. If the execution of ``quil_program`` is **non-deterministic** then the
final wavefunction only represents a stochastically generated sample and the
wavefunctions returned by *different* ``wavefunction`` calls *will generally be
different*.
:param quil_program: A Quil program.
:param memory_map: An assignment of classical registers to values, representing an initial
state for the QAM's classical memory.
This is expected to be of type Dict[str, List[Union[int, float]]],
where the keys are memory region names and the values are arrays of
initialization data.
:return: A Wavefunction object representing the state of the QVM.
"""
if memory_map is not None:
quil_program = self.augment_program_with_memory_values(
quil_program, memory_map)
request = self._wavefunction_request(quil_program=quil_program)
response = self._qvm_client.get_wavefunction(request)
return Wavefunction.from_bit_packed_string(response.wavefunction)
def expectation(
self,
prep_prog: Program,
pauli_terms: Union[PauliSum, List[PauliTerm]],
memory_map: Optional[Dict[str, List[Union[int, float]]]] = None,
) -> Union[float, np.ndarray]:
"""
Calculate the expectation value of Pauli operators given a state prepared by prep_program.
If ``pauli_terms`` is a ``PauliSum`` then the returned value is a single ``float``,
otherwise the returned value is an array of values, one for each ``PauliTerm`` in the
list.
.. note:: If your program contains measurements or noisy gates, this method may not do what
you want. If the execution of ``quil_program`` is **non-deterministic** then the
final wavefunction from which the expectation value is calculated only represents
a stochastically generated sample and the wavefunctions returned by *different*
``wavefunction`` calls *will generally be different*.
:param prep_prog: A program that prepares the state on which we measure the expectation.
:param pauli_terms: A Pauli representation of a quantum operator.
:param memory_map: An assignment of classical registers to values, representing an initial
state for the QAM's classical memory.
This is expected to be of type Dict[str, List[Union[int, float]]],
where the keys are memory region names and the values are arrays of
initialization data.
:return: Either a float or array floats depending on ``pauli_terms``.
"""
is_pauli_sum = False
if isinstance(pauli_terms, PauliSum):
progs, coeffs = pauli_terms.get_programs()
is_pauli_sum = True
else:
coeffs = np.array([pt.coefficient for pt in pauli_terms])
progs = [pt.program for pt in pauli_terms]
if memory_map is not None:
prep_prog = self.augment_program_with_memory_values(
prep_prog, memory_map)
bare_results = self._expectation(prep_prog, progs)
results = coeffs * bare_results
if is_pauli_sum:
return np.sum(results) # type: ignore
return results # type: ignore
def _expectation(self, prep_prog: Program,
operator_programs: Iterable[Program]) -> np.ndarray:
if isinstance(operator_programs, Program):
warnings.warn(
"You have provided a Program rather than a list of Programs. The results "
"from expectation will be line-wise expectation values of the "
"operator_programs.",
SyntaxWarning,
)
request = self._expectation_request(
prep_prog=prep_prog,
operator_programs=operator_programs,
)
response = self._qvm_client.measure_expectation(request)
return np.asarray(response.expectations)
def run_and_measure(
self,
quil_program: Program,
qubits: Optional[List[int]] = None,
trials: int = 1,
memory_map: Optional[Dict[str, List[Union[int, float]]]] = None,
) -> np.ndarray:
"""
Run a Quil program once to determine the final wavefunction, and measure multiple times.
Alternatively, consider using ``wavefunction`` and calling ``sample_bitstrings`` on the
resulting object.
For a large wavefunction and a low-medium number of trials, use this function.
On the other hand, if you're sampling a small system many times you might want to
use ``Wavefunction.sample_bitstrings``.
.. note:: If your program contains measurements or noisy gates, this method may not do what
you want. If the execution of ``quil_program`` is **non-deterministic** then the
final wavefunction from which the returned bitstrings are sampled itself only
represents a stochastically generated sample and the outcomes sampled from
*different* ``run_and_measure`` calls *generally sample different bitstring
distributions*.
:param quil_program: The program to run and measure
:param qubits: An optional list of qubits to measure. The order of this list is
respected in the returned bitstrings. If not provided, all qubits used in
the program will be measured and returned in their sorted order.
:param int trials: Number of times to sample from the prepared wavefunction.
:param memory_map: An assignment of classical registers to values, representing an initial
state for the QAM's classical memory.
This is expected to be of type Dict[str, List[Union[int, float]]],
where the keys are memory region names and the values are arrays of
initialization data.
:return: An array of measurement results (0 or 1) of shape (trials, len(qubits))
"""
if qubits is None:
qubits = sorted(
cast(Set[int], quil_program.get_qubits(indices=True)))
if memory_map is not None:
quil_program = self.augment_program_with_memory_values(
quil_program, memory_map)
request = self._run_and_measure_request(
quil_program=quil_program,
qubits=qubits,
trials=trials,
)
response = self._qvm_client.run_and_measure_program(request)
return np.asarray(response.results)
@staticmethod
def augment_program_with_memory_values(
quil_program: Program,
memory_map: Dict[str, List[Union[int, float]]],
) -> Program:
p = Program()
# we stupidly allowed memory_map to be of type Dict[MemoryReference, Any], whereas qc.run
# takes a memory initialization argument of type Dict[str, List[Union[int, float]]. until
# we are in a position to remove this, we support both styles of input.
if len(memory_map.keys()) == 0:
return quil_program
elif isinstance(list(memory_map.keys())[0], str):
for name, arr in memory_map.items():
for index, value in enumerate(arr):
p += MOVE(MemoryReference(name, offset=index), value)
else:
raise TypeError(
"Bad memory_map type; expected Dict[str, List[Union[int, float]]]."
)
p += quil_program
return percolate_declares(p)
def _run_and_measure_request(
self,
*,
quil_program: Program,
qubits: Sequence[int],
trials: int,
) -> RunAndMeasureProgramRequest:
if not quil_program:
raise ValueError("Cannot execute an empty program")
if not isinstance(quil_program, Program):
raise TypeError(
f"quil_program must be a Program object, got type {type(quil_program)}"
)
qubits = validate_qubit_list(qubits)
if not isinstance(trials, int):
raise TypeError(
f"trials must be an integer, got type {type(trials)}")
return RunAndMeasureProgramRequest(
program=quil_program.out(calibrations=False),
qubits=list(qubits),
trials=trials,
measurement_noise=self.measurement_noise,
gate_noise=self.gate_noise,
seed=self.random_seed,
)
def _wavefunction_request(
self,
*,
quil_program: Program,
) -> GetWavefunctionRequest:
if not isinstance(quil_program, Program):
raise TypeError(
f"quil_program must be a Program object, got type {type(quil_program)}"
)
return GetWavefunctionRequest(
program=quil_program.out(calibrations=False),
measurement_noise=self.measurement_noise,
gate_noise=self.gate_noise,
seed=self.random_seed,
)
def _expectation_request(
self,
*,
prep_prog: Program,
operator_programs: Optional[Iterable[Program]],
) -> MeasureExpectationRequest:
if operator_programs is None:
operator_programs = [Program()]
if not isinstance(prep_prog, Program):
raise TypeError(
f"prep_prog must be a Program object, got type {type(prep_prog)}"
)
return MeasureExpectationRequest(
prep_program=prep_prog.out(calibrations=False),
pauli_operators=[
x.out(calibrations=False) for x in operator_programs
],
seed=self.random_seed,
)