def convert(self, op: ops.Operation) -> ops.OP_TREE:
return protocols.decompose(
op,
intercepting_decomposer=self._convert_one,
keep=self._keep,
on_stuck_raise=(None if self.ignore_failures else self._on_stuck_raise),
)
def convert(self, op: 'cirq.Operation') -> List['cirq.Operation']:
a = protocols.decompose(op,
keep=is_sqrt_iswap_compatible,
intercepting_decomposer=self._convert_one,
on_stuck_raise=(None if self.ignore_failures
else self._on_stuck_raise))
return a
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True,
) -> Iterator[simulator.StepResult]:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
state = wave_function.to_valid_state_vector(initial_state, num_qubits,
self._dtype)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate unknown operations that don't specify a "
"_unitary_ method, a _decompose_ method, or "
"(_has_unitary_ + _apply_unitary_) methods"
": {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
return (protocols.has_unitary(potential_op)
or ops.MeasurementGate.is_measurement(potential_op))
state = np.reshape(state, (2, ) * num_qubits)
buffer = np.empty((2, ) * num_qubits, dtype=self._dtype)
for moment in circuit:
measurements = collections.defaultdict(
list) # type: Dict[str, List[bool]]
unitary_ops_and_measurements = protocols.decompose(
moment.operations, keep=keep, on_stuck_raise=on_stuck)
for op in unitary_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
if ops.MeasurementGate.is_measurement(op):
gate = cast(ops.MeasurementGate,
cast(ops.GateOperation, op).gate)
if perform_measurements:
invert_mask = gate.invert_mask or num_qubits * (
False, )
# Measure updates inline.
bits, _ = wave_function.measure_state_vector(
state, indices, state)
corrected = [
bit ^ mask for bit, mask in zip(bits, invert_mask)
]
measurements[cast(str, gate.key)].extend(corrected)
else:
result = protocols.apply_unitary(
op,
args=protocols.ApplyUnitaryArgs(
state, buffer, indices))
if result is buffer:
buffer = state
state = result
yield SimulatorStep(state, measurements, qubit_map, self._dtype)
def decompose_operation(self,
operation: 'cirq.Operation') -> 'cirq.OP_TREE':
if self.is_native_or_final(operation):
return operation
return protocols.decompose(
operation,
intercepting_decomposer=self.operation_decomposer,
keep=self.is_native_or_final,
on_stuck_raise=None)
def optimization_at(self, circuit, index, op):
decomposition = protocols.decompose(op, keep=self.no_decomp,
on_stuck_raise=None)
if decomposition is op:
return None
return PointOptimizationSummary(
clear_span=1,
clear_qubits=op.qubits,
new_operations=decomposition)
def optimization_at(
self, circuit: 'cirq.Circuit', index: int,
op: 'cirq.Operation') -> Optional['cirq.PointOptimizationSummary']:
decomposition = protocols.decompose(op,
keep=self.no_decomp,
on_stuck_raise=None)
if decomposition == [op]:
return None
return PointOptimizationSummary(clear_span=1,
clear_qubits=op.qubits,
new_operations=decomposition)
def assert_decompose_ends_at_default_gateset(val: Any):
"""Asserts that cirq.decompose(val) ends at default cirq gateset or a known gate."""
if _known_gate_with_no_decomposition(val):
return # coverage: ignore
args = () if isinstance(val, ops.Operation) else (tuple(
devices.LineQid.for_gate(val)), )
dec_once = protocols.decompose_once(val, [val(*args[0]) if args else val],
*args)
for op in [*ops.flatten_to_ops(protocols.decompose(d) for d in dec_once)]:
assert _known_gate_with_no_decomposition(op.gate) or (
op in protocols.decompose_protocol.DECOMPOSE_TARGET_GATESET
), f'{val} decomposed to {op}, which is not part of default cirq target gateset.'
def convert(self, op: ops.Operation) -> List[ops.Operation]:
def on_stuck_raise(bad):
return TypeError("Don't know how to work with {!r}. "
"It isn't a native xmon operation, "
"a 1 or 2 qubit gate with a known unitary, "
"or composite.".format(bad))
return protocols.decompose(
op,
keep=xmon_gates.is_native_xmon_op,
intercepting_decomposer=self._convert_one,
on_stuck_raise=None if self.ignore_failures else on_stuck_raise)
def _write_operations(
self,
op_tree: 'cirq.OP_TREE',
output: Callable[[str], None],
output_line_gap: Callable[[int], None],
) -> None:
def keep(op: 'cirq.Operation') -> bool:
return protocols.qasm(op, args=self.args, default=None) is not None
def fallback(op):
if len(op.qubits) not in [1, 2]:
return NotImplemented
mat = protocols.unitary(op, None)
if mat is None:
return NotImplemented
if len(op.qubits) == 1:
return QasmUGate.from_matrix(mat).on(*op.qubits)
return QasmTwoQubitGate.from_matrix(mat).on(*op.qubits)
def on_stuck(bad_op):
return ValueError(
'Cannot output operation as QASM: {!r}'.format(bad_op))
for main_op in ops.flatten_op_tree(op_tree):
decomposed = protocols.decompose(main_op,
keep=keep,
fallback_decomposer=fallback,
on_stuck_raise=on_stuck)
qasms = [protocols.qasm(op, args=self.args) for op in decomposed]
should_annotate = decomposed != [main_op
] or qasms[0].count('\n') > 1
if should_annotate:
output_line_gap(1)
if isinstance(main_op, ops.GateOperation):
x = str(main_op.gate).replace('\n', '\n //')
output('// Gate: {!s}\n'.format(x))
else:
x = str(main_op).replace('\n', '\n //')
output('// Operation: {!s}\n'.format(x))
for qasm in qasms:
output(qasm)
if should_annotate:
output_line_gap(1)
def __init__(self,
circuit: circuits.Circuit,
initial_state: Union[int, np.ndarray]):
self.grouping = _QubitGrouping(circuit)
self.circuit = circuits.Circuit(
protocols.decompose(
circuit,
intercepting_decomposer=self.grouping.intercept_decompose_func,
keep=self.grouping.decompose_keep_func))
self.layers = _circuit_as_layers(self.circuit, self.grouping)
self.feed_dict = {} # type: Dict[tf.Tensor, Any]
# Capture initial state.
self._initial_state_func = self._pick_initial_state_func(initial_state)
# Store list of CZ indices for each layer.
max_czs = 0
if self.layers:
max_czs = max(len(e.cz_indices) for e in self.layers)
max_czs = max(1, max_czs)
self.secret_cz_indices = tf.placeholder(
name='cz_indices',
dtype=tf.int32,
shape=(len(self.layers), max_czs, 2))
padded_cz_indices = np.array(
[
e.cz_indices + [(-1, -1)] * (max_czs - len(e.cz_indices))
for e in self.layers
],
dtype=np.int32
).reshape((len(self.layers), max_czs, 2))
self.feed_dict[self.secret_cz_indices] = padded_cz_indices
# Store matrices for each qubit group at each layer.
self.secret_group_matrices = [
tf.placeholder(
name='group_matrices_{}'.format(i),
dtype=tf.complex64,
shape=(len(self.layers),
1 << len(g),
1 << len(g)))
for i, g in enumerate(self.grouping.groups)
]
for i in range(len(self.grouping.groups)):
j = len(self.grouping.groups) - i - 1
self.feed_dict[self.secret_group_matrices[j]] = np.array([
e.group_matrices[j]
for e in self.layers
])
def optimization_at(
self, circuit: circuits.Circuit, index: int,
op: ops.Operation) -> Optional[circuits.PointOptimizationSummary]:
converted = protocols.decompose(
op,
intercepting_decomposer=self._decompose_two_qubit_unitaries,
keep=self._keep,
on_stuck_raise=(None
if self.ignore_failures else self._on_stuck_raise))
if converted == [op]:
return None
return circuits.PointOptimizationSummary(clear_span=1,
new_operations=converted,
clear_qubits=op.qubits)
def _write_quil(self, output_func: Callable[[str], None]) -> None:
output_func('# Created using Cirq.\n\n')
if len(self.measurements) > 0:
measurements_declared: Set[str] = set()
for m in self.measurements:
key = protocols.measurement_key(m)
if key in measurements_declared:
continue
measurements_declared.add(key)
output_func(
f'DECLARE {self.measurement_id_map[key]} BIT[{len(m.qubits)}]\n'
)
output_func('\n')
def keep(op: 'cirq.Operation') -> bool:
return protocols.quil(op, formatter=self.formatter) is not None
def fallback(op):
if len(op.qubits) not in [1, 2]:
return NotImplemented
mat = protocols.unitary(op, None)
if mat is None:
return NotImplemented
# Following code is a safety measure
# Could not find a gate that doesn't decompose into a gate
# with a _quil_ implementation
# coverage: ignore
if len(op.qubits) == 1:
return QuilOneQubitGate(mat).on(*op.qubits)
return QuilTwoQubitGate(mat).on(*op.qubits)
def on_stuck(bad_op):
return ValueError(f'Cannot output operation as QUIL: {bad_op!r}')
for main_op in self.operations:
decomposed = protocols.decompose(main_op,
keep=keep,
fallback_decomposer=fallback,
on_stuck_raise=on_stuck)
for decomposed_op in decomposed:
output_func(
protocols.quil(decomposed_op, formatter=self.formatter))
def decompose_operation(self, operation: 'ops.Operation') -> 'ops.OP_TREE':
if isinstance(operation.gate, self.auto_decompose_gates):
return protocols.decompose(operation)
return operation
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
matrix = density_matrix_utils.to_valid_density_matrix(
initial_state, num_qubits, self._dtype)
if len(circuit) == 0:
yield DensityMatrixStepResult(matrix, {}, qubit_map, self._dtype)
matrix = np.reshape(matrix, (2,) * num_qubits * 2)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate operations that don't implement "
"SupportsUnitary, SupportsApplyUnitary, SupportsMixture, "
"SupportsChannel or is a measurement: {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
return (protocols.has_channel(potential_op)
or (ops.op_gate_of_type(potential_op,
ops.MeasurementGate) is not None)
or isinstance(potential_op,
(ops.SamplesDisplay,
ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay))
)
matrix = np.reshape(matrix, (2,) * num_qubits * 2)
noisy_moments = self.noise.noisy_moments(circuit,
sorted(circuit.all_qubits()))
for moment in noisy_moments:
measurements = collections.defaultdict(
list) # type: Dict[str, List[bool]]
channel_ops_and_measurements = protocols.decompose(
moment, keep=keep, on_stuck_raise=on_stuck)
for op in channel_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
if isinstance(op,
(ops.SamplesDisplay,
ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)):
continue
# TODO: support more general measurements.
meas = ops.op_gate_of_type(op, ops.MeasurementGate)
if meas:
if perform_measurements:
invert_mask = meas.invert_mask or num_qubits * (False,)
# Measure updates inline.
bits, _ = density_matrix_utils.measure_density_matrix(
matrix, indices, matrix)
corrected = [bit ^ mask for bit, mask in
zip(bits, invert_mask)]
key = protocols.measurement_key(meas)
measurements[key].extend(corrected)
else:
# TODO: Use apply_channel similar to apply_unitary.
gate = cast(ops.GateOperation, op).gate
channel = protocols.channel(gate)
sum_buffer = np.zeros((2,) * 2 * num_qubits,
dtype=self._dtype)
buffer = np.empty((2,) * 2 * num_qubits, dtype=self._dtype)
out = np.empty((2,) * 2 * num_qubits, dtype=self._dtype)
for krauss in channel:
krauss_tensor = np.reshape(krauss.astype(self._dtype),
(2,) * gate.num_qubits() * 2)
result = linalg.targeted_conjugate_about(krauss_tensor,
matrix,
indices,
buffer=buffer,
out=out)
sum_buffer += result
np.copyto(dst=matrix, src=sum_buffer)
yield DensityMatrixStepResult(
density_matrix=matrix,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def _decompose_(self) -> 'cirq.OP_TREE':
return protocols.decompose(self.sub_operation)
def _base_iterator(self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
matrix = density_matrix_utils.to_valid_density_matrix(
initial_state, num_qubits, self._dtype)
if len(circuit) == 0:
yield DensityMatrixStepResult(matrix, {}, qubit_map, self._dtype)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate operations that don't implement "
"SupportsUnitary, SupportsApplyUnitary, SupportsMixture, "
"SupportsChannel or is a measurement: {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
return (protocols.has_channel(potential_op)
or (ops.op_gate_of_type(potential_op, ops.MeasurementGate)
is not None)
or isinstance(potential_op,
(ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)))
matrix = np.reshape(matrix, (2**num_qubits, 2**num_qubits))
noisy_moments = self.noise.noisy_moments(circuit,
sorted(circuit.all_qubits()))
state = qulacs.DensityMatrix(num_qubits)
state.load(matrix)
for moment in noisy_moments:
measurements = collections.defaultdict(
list) # type: Dict[str, List[bool]]
channel_ops_and_measurements = protocols.decompose(
moment, keep=keep, on_stuck_raise=on_stuck)
for op in channel_ops_and_measurements:
#indices = [qubit_map[qubit] for qubit in op.qubits]
indices = [
num_qubits - 1 - qubit_map[qubit] for qubit in op.qubits
]
indices.reverse()
if isinstance(op, (ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)):
continue
# TODO: support more general measurements.
meas = ops.op_gate_of_type(op, ops.MeasurementGate)
if meas:
# Not implemented
raise NotImplementedError(
"Measurement is not supported in qulacs simulator")
else:
# TODO: Use apply_channel similar to apply_unitary.
gate = cast(ops.GateOperation, op).gate
channel = protocols.channel(gate)
qulacs_gates = []
for krauss in channel:
krauss = krauss.astype(np.complex128)
qulacs_gate = qulacs.gate.DenseMatrix(indices, krauss)
qulacs_gates.append(qulacs_gate)
qulacs_cptp_map = qulacs.gate.CPTP(qulacs_gates)
qulacs_cptp_map.update_quantum_state(state)
matrix = state.get_matrix()
matrix = np.reshape(matrix, (2, ) * num_qubits * 2)
yield DensityMatrixStepResult(density_matrix=matrix,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: 'cirq.STATE_VECTOR_LIKE',
perform_measurements: bool = True,
) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qid_shape = protocols.qid_shape(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
state = qis.to_valid_state_vector(initial_state,
num_qubits,
qid_shape=qid_shape,
dtype=self._dtype)
if len(circuit) == 0:
yield SparseSimulatorStep(state, {}, qubit_map, self._dtype)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate unknown operations that don't specify a "
"_unitary_ method, a _decompose_ method, "
"(_has_unitary_ + _apply_unitary_) methods,"
"(_has_mixture_ + _mixture_) methods, or are measurements."
": {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
# The order of this is optimized to call has_xxx methods first.
return (protocols.has_unitary(potential_op) or
protocols.has_mixture(potential_op) or
protocols.is_measurement(potential_op) or
isinstance(potential_op.gate, ops.ResetChannel))
data = _StateAndBuffer(state=np.reshape(state, qid_shape),
buffer=np.empty(qid_shape, dtype=self._dtype))
for moment in circuit:
measurements = collections.defaultdict(
list) # type: Dict[str, List[int]]
unitary_ops_and_measurements = protocols.decompose(
moment, keep=keep, on_stuck_raise=on_stuck)
for op in unitary_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
if isinstance(op.gate, ops.ResetChannel):
self._simulate_reset(op, data, indices)
elif protocols.has_unitary(op):
self._simulate_unitary(op, data, indices)
elif protocols.is_measurement(op):
# Do measurements second, since there may be mixtures that
# operate as measurements.
# TODO: support measurement outside the computational basis.
# Github issue:
# https://github.com/quantumlib/Cirq/issues/1357
if perform_measurements:
self._simulate_measurement(op, data, indices,
measurements, num_qubits)
elif protocols.has_mixture(op):
self._simulate_mixture(op, data, indices)
yield SparseSimulatorStep(
state_vector=data.state,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True,
) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
state = wave_function.to_valid_state_vector(initial_state, num_qubits,
self._dtype)
if len(circuit) == 0:
yield SparseSimulatorStep(state, {}, qubit_map, self._dtype)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate unknown operations that don't specify a "
"_unitary_ method, a _decompose_ method, or "
"(_has_unitary_ + _apply_unitary_) methods"
": {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
return (protocols.has_unitary(potential_op)
or protocols.is_measurement(potential_op))
state = np.reshape(state, (2, ) * num_qubits)
buffer = np.empty((2, ) * num_qubits, dtype=self._dtype)
for moment in circuit:
measurements = collections.defaultdict(
list) # type: Dict[str, List[bool]]
non_display_ops = (op for op in moment
if not isinstance(op, (
ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)))
unitary_ops_and_measurements = protocols.decompose(
non_display_ops, keep=keep, on_stuck_raise=on_stuck)
for op in unitary_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
# TODO: Support measurements outside of computational basis.
# TODO: Support mixtures.
meas = ops.op_gate_of_type(op, ops.MeasurementGate)
if meas:
if perform_measurements:
invert_mask = meas.invert_mask or num_qubits * (
False, )
# Measure updates inline.
bits, _ = wave_function.measure_state_vector(
state, indices, state)
corrected = [
bit ^ mask for bit, mask in zip(bits, invert_mask)
]
key = protocols.measurement_key(meas)
measurements[key].extend(corrected)
else:
result = protocols.apply_unitary(
op,
args=protocols.ApplyUnitaryArgs(
state, buffer, indices))
if result is buffer:
buffer = state
state = result
yield SparseSimulatorStep(state_vector=state,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[np.ndarray, 'cirq.STATE_VECTOR_LIKE'],
all_measurements_are_terminal=False,
is_raw_state=False,
) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
qid_shape = protocols.qid_shape(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
initial_matrix = (qis.to_valid_density_matrix(initial_state,
len(qid_shape),
qid_shape=qid_shape,
dtype=self._dtype)
if not is_raw_state else initial_state)
if np.may_share_memory(initial_matrix, initial_state):
initial_matrix = initial_matrix.copy()
measured = collections.defaultdict(
bool) # type: Dict[Tuple[cirq.Qid, ...], bool]
if len(circuit) == 0:
yield DensityMatrixStepResult(initial_matrix, {}, qubit_map,
self._dtype)
return
state = _StateAndBuffers(
len(qid_shape),
initial_matrix.reshape(qid_shape *
2) if not is_raw_state else initial_matrix,
)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate operations that don't implement "
"SupportsUnitary, SupportsConsistentApplyUnitary, "
"SupportsMixture, SupportsChannel or is a measurement: {!r}".
format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
return protocols.has_channel(
potential_op, allow_decompose=False) or isinstance(
potential_op.gate, ops.MeasurementGate)
noisy_moments = self.noise.noisy_moments(circuit,
sorted(circuit.all_qubits()))
for moment in noisy_moments:
measurements = collections.defaultdict(
list) # type: Dict[str, List[int]]
channel_ops_and_measurements = protocols.decompose(
moment, keep=keep, on_stuck_raise=on_stuck)
for op in channel_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
# TODO: support more general measurements.
# Github issue: https://github.com/quantumlib/Cirq/issues/1357
if all_measurements_are_terminal and measured[op.qubits]:
continue
if isinstance(op.gate, ops.MeasurementGate):
measured[op.qubits] = True
meas = op.gate
if all_measurements_are_terminal:
continue
if self._ignore_measurement_results:
for i, q in enumerate(op.qubits):
self._apply_op_channel(
ops.phase_damp(1).on(q), state, [indices[i]])
else:
invert_mask = meas.full_invert_mask()
# Measure updates inline.
bits, _ = density_matrix_utils.measure_density_matrix(
state.tensor,
indices,
qid_shape=qid_shape,
out=state.tensor,
seed=self._prng,
)
corrected = [
bit ^ (bit < 2 and mask)
for bit, mask in zip(bits, invert_mask)
]
key = protocols.measurement_key(meas)
measurements[key].extend(corrected)
else:
self._apply_op_channel(op, state, indices)
yield DensityMatrixStepResult(
density_matrix=state.tensor,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype,
)
def map_func(op: 'cirq.Operation', _) -> 'cirq.OP_TREE':
if context and context.deep and isinstance(op.untagged,
circuits.CircuitOperation):
return op
return protocols.decompose(op, keep=no_decomp, on_stuck_raise=None)
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True,
) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
state = wave_function.to_valid_state_vector(initial_state, num_qubits,
self._dtype)
if len(circuit) == 0:
yield SparseSimulatorStep(state, {}, qubit_map, self._dtype)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate unknown operations that don't specify a "
"_unitary_ method, a _decompose_ method, "
"(_has_unitary_ + _apply_unitary_) methods,"
"(_has_mixture_ + _mixture_) methods, or are measurements."
": {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
# The order of this is optimized to call has_xxx methods first.
return (protocols.has_unitary(potential_op)
or protocols.has_mixture(potential_op)
or protocols.is_measurement(potential_op))
data = _StateAndBuffer(state=np.reshape(state, (2, ) * num_qubits),
buffer=np.empty((2, ) * num_qubits,
dtype=self._dtype))
for moment in circuit:
measurements = collections.defaultdict(
list) # type: Dict[str, List[bool]]
non_display_ops = (op for op in moment
if not isinstance(op, (
ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)))
unitary_ops_and_measurements = protocols.decompose(
non_display_ops, keep=keep, on_stuck_raise=on_stuck)
for op in unitary_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
if protocols.has_unitary(op):
self._simulate_unitary(op, data, indices)
elif protocols.is_measurement(op):
# Do measurements second, since there may be mixtures that
# operate as measurements.
# TODO: support measurement outside the computational basis.
if perform_measurements:
self._simulate_measurement(op, data, indices,
measurements, num_qubits)
elif protocols.has_mixture(op):
self._simulate_mixture(op, data, indices)
yield SparseSimulatorStep(state_vector=data.state,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True,
) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
num_qubits = len(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
state = wave_function.to_valid_state_vector(initial_state, num_qubits,
self._dtype)
if len(circuit) == 0:
yield SparseSimulatorStep(state, {}, qubit_map, self._dtype)
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate unknown operations that don't specify a "
"_unitary_ method, a _decompose_ method, "
"(_has_unitary_ + _apply_unitary_) methods,"
"(_has_mixture_ + _mixture_) methods, or are measurements."
": {!r}".format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
# The order of this is optimized to call has_xxx methods first.
return (protocols.has_unitary(potential_op)
or protocols.has_mixture(potential_op)
or protocols.is_measurement(potential_op))
data = _StateAndBuffer(state=np.reshape(state, (2, ) * num_qubits),
buffer=np.empty((2, ) * num_qubits,
dtype=self._dtype))
shape = np.array(data.state).shape
# Qulacs
qulacs_flag = 0
qulacs_state = qulacs.QuantumStateGpu(int(num_qubits))
qulacs_circuit = qulacs.QuantumCircuit(int(num_qubits))
for moment in circuit:
measurements = collections.defaultdict(
list) # type: Dict[str, List[bool]]
non_display_ops = (op for op in moment
if not isinstance(op, (
ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)))
unitary_ops_and_measurements = protocols.decompose(
non_display_ops, keep=keep, on_stuck_raise=on_stuck)
for op in unitary_ops_and_measurements:
indices = [
num_qubits - 1 - qubit_map[qubit] for qubit in op.qubits
]
if protocols.has_unitary(op):
# single qubit unitary gates
if isinstance(op.gate, ops.pauli_gates._PauliX):
qulacs_circuit.add_X_gate(indices[0])
elif isinstance(op.gate, ops.pauli_gates._PauliY):
qulacs_circuit.add_Y_gate(indices[0])
elif isinstance(op.gate, ops.pauli_gates._PauliZ):
qulacs_circuit.add_Z_gate(indices[0])
elif isinstance(op.gate, ops.common_gates.HPowGate):
qulacs_circuit.add_H_gate(indices[0])
elif isinstance(op.gate, ops.common_gates.XPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.common_gates.YPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.common_gates.ZPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, circuits.qasm_output.QasmUGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate,
ops.matrix_gates.SingleQubitMatrixGate):
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
# Two Qubit Unitary Gates
elif isinstance(op.gate, ops.common_gates.CNotPowGate):
qulacs_circuit.add_CNOT_gate(indices[0], indices[1])
elif isinstance(op.gate, ops.common_gates.CZPowGate):
qulacs_circuit.add_CZ_gate(indices[0], indices[1])
elif isinstance(op.gate, ops.common_gates.SwapPowGate):
qulacs_circuit.add_SWAP_gate(indices[0], indices[1])
elif isinstance(op.gate, ops.common_gates.ISwapPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.parity_gates.XXPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.parity_gates.YYPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.parity_gates.ZZPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate,
ops.matrix_gates.TwoQubitMatrixGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
# Three Qubit Unitary Gates
elif isinstance(op.gate, ops.three_qubit_gates.CCXPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.three_qubit_gates.CCZPowGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
elif isinstance(op.gate, ops.three_qubit_gates.CSwapGate):
indices.reverse()
qulacs_circuit.add_dense_matrix_gate(
indices, op._unitary_())
qulacs_flag = 1
elif protocols.is_measurement(op):
# Do measurements second, since there may be mixtures that
# operate as measurements.
# TODO: support measurement outside the computational basis.
if perform_measurements:
if qulacs_flag == 1:
self._simulate_on_qulacs(data, shape, qulacs_state,
qulacs_circuit)
qulacs_flag = 0
self._simulate_measurement(op, data, indices,
measurements, num_qubits)
elif protocols.has_mixture(op):
if qulacs_flag == 1:
self._simulate_on_qulacs(data, shape, qulacs_state,
qulacs_circuit)
qulacs_flag = 0
qulacs_circuit = qulacs.QuantumCircuit(int(num_qubits))
self._simulate_mixture(op, data, indices)
if qulacs_flag == 1:
self._simulate_on_qulacs(data, shape, qulacs_state, qulacs_circuit)
qulacs_flag = 0
del qulacs_state
del qulacs_circuit
yield SparseSimulatorStep(state_vector=data.state,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def _base_iterator(self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: Union[int, np.ndarray],
perform_measurements: bool = True) -> Iterator:
qubits = ops.QubitOrder.as_qubit_order(qubit_order).order_for(
circuit.all_qubits())
qid_shape = protocols.qid_shape(qubits)
qubit_map = {q: i for i, q in enumerate(qubits)}
initial_matrix = density_matrix_utils.to_valid_density_matrix(
initial_state,
len(qid_shape),
qid_shape=qid_shape,
dtype=self._dtype)
if len(circuit) == 0:
yield DensityMatrixStepResult(initial_matrix, {}, qubit_map,
self._dtype)
return
state = _StateAndBuffers(len(qid_shape),
initial_matrix.reshape(qid_shape * 2))
def on_stuck(bad_op: ops.Operation):
return TypeError(
"Can't simulate operations that don't implement "
"SupportsUnitary, SupportsConsistentApplyUnitary, "
"SupportsMixture, SupportsChannel or is a measurement: {!r}".
format(bad_op))
def keep(potential_op: ops.Operation) -> bool:
return (protocols.has_channel(potential_op)
or (ops.op_gate_of_type(potential_op, ops.MeasurementGate)
is not None)
or isinstance(potential_op,
(ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)))
noisy_moments = self.noise.noisy_moments(circuit,
sorted(circuit.all_qubits()))
for moment in noisy_moments:
measurements = collections.defaultdict(
list) # type: Dict[str, List[int]]
channel_ops_and_measurements = protocols.decompose(
moment, keep=keep, on_stuck_raise=on_stuck)
for op in channel_ops_and_measurements:
indices = [qubit_map[qubit] for qubit in op.qubits]
if isinstance(op, (ops.SamplesDisplay, ops.WaveFunctionDisplay,
ops.DensityMatrixDisplay)):
continue
# TODO: support more general measurements.
meas = ops.op_gate_of_type(op, ops.MeasurementGate)
if meas:
if perform_measurements:
invert_mask = meas.full_invert_mask()
# Measure updates inline.
bits, _ = density_matrix_utils.measure_density_matrix(
state.tensor,
indices,
qid_shape=qid_shape,
out=state.tensor)
corrected = [
bit ^ (bit < 2 and mask)
for bit, mask in zip(bits, invert_mask)
]
key = protocols.measurement_key(meas)
measurements[key].extend(corrected)
else:
# TODO: Use apply_channel similar to apply_unitary.
self._apply_op_channel(op, state, indices)
yield DensityMatrixStepResult(density_matrix=state.tensor,
measurements=measurements,
qubit_map=qubit_map,
dtype=self._dtype)
def map_func(op: 'cirq.Operation', _) -> 'cirq.OP_TREE':
return protocols.decompose(op, keep=no_decomp, on_stuck_raise=None)