def state_vector_has_stabilizer(state_vector: np.ndarray,
stabilizer: DensePauliString) -> bool:
"""Checks that the state_vector is stabilized by the given stabilizer.
The stabilizer should not modify the value of the state_vector, up to the
global phase.
Args:
state_vector: An input state vector. Is not mutated by this function.
stabilizer: A potential stabilizer of the above state_vector as a
DensePauliString.
Returns:
Whether the stabilizer stabilizes the supplied state.
"""
args = act_on_state_vector_args.ActOnStateVectorArgs(
target_tensor=state_vector.copy(),
available_buffer=np.empty_like(state_vector),
axes=range(protocols.num_qubits(stabilizer)),
prng=np.random.RandomState(),
log_of_measurement_results={},
)
protocols.act_on(stabilizer, args)
return np.allclose(args.target_tensor, state_vector)
def _create_act_on_args(
self,
initial_state: Union['cirq.STATE_VECTOR_LIKE', 'cirq.ActOnStateVectorArgs'],
qubits: Sequence['cirq.Qid'],
):
"""Creates the ActOnStateVectorArgs for a circuit.
Args:
initial_state: The initial state for the simulation in the
computational basis.
qubits: 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:
ActOnStateVectorArgs for the circuit.
"""
if isinstance(initial_state, act_on_state_vector_args.ActOnStateVectorArgs):
return initial_state
num_qubits = len(qubits)
qid_shape = protocols.qid_shape(qubits)
state = qis.to_valid_state_vector(
initial_state, num_qubits, qid_shape=qid_shape, dtype=self._dtype
)
return act_on_state_vector_args.ActOnStateVectorArgs(
target_tensor=np.reshape(state, qid_shape),
available_buffer=np.empty(qid_shape, dtype=self._dtype),
qubits=qubits,
axes=[],
prng=self._prng,
log_of_measurement_results={},
)
def _create_partial_act_on_args(
self,
initial_state: Union['cirq.STATE_VECTOR_LIKE',
'cirq.ActOnStateVectorArgs'],
qubits: Sequence['cirq.Qid'],
classical_data: 'cirq.ClassicalDataStore',
):
"""Creates the ActOnStateVectorArgs for a circuit.
Args:
initial_state: The initial state for the simulation in the
computational basis.
qubits: 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.
classical_data: The shared classical data container for this
simulation.
Returns:
ActOnStateVectorArgs for the circuit.
"""
if isinstance(initial_state,
act_on_state_vector_args.ActOnStateVectorArgs):
return initial_state
return act_on_state_vector_args.ActOnStateVectorArgs(
qubits=qubits,
prng=self._prng,
classical_data=classical_data,
initial_state=initial_state,
dtype=self._dtype,
)
def state_vector_has_stabilizer(state_vector: np.ndarray,
stabilizer: DensePauliString) -> bool:
"""Checks that the stabilizer does not modify the value of the
state_vector, including the global phase. Does not mutate the input
state_vector."""
args = act_on_state_vector_args.ActOnStateVectorArgs(
target_tensor=state_vector.copy(),
available_buffer=np.empty_like(state_vector),
axes=range(protocols.num_qubits(stabilizer)),
prng=np.random.RandomState(),
log_of_measurement_results={})
protocols.act_on(stabilizer, args)
return np.allclose(args.target_tensor, state_vector)
def _base_iterator(
self,
circuit: circuits.Circuit,
qubit_order: ops.QubitOrderOrList,
initial_state: 'cirq.STATE_VECTOR_LIKE',
perform_measurements: bool = True,
) -> Iterator['SparseSimulatorStep']:
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)
sim_state = act_on_state_vector_args.ActOnStateVectorArgs(
target_tensor=np.reshape(state, qid_shape),
available_buffer=np.empty(qid_shape, dtype=self._dtype),
axes=[],
prng=self._prng,
log_of_measurement_results={},
)
noisy_moments = self.noise.noisy_moments(circuit,
sorted(circuit.all_qubits()))
for op_tree in noisy_moments:
for op in flatten_to_ops(op_tree):
if perform_measurements or not isinstance(
op.gate, ops.MeasurementGate):
sim_state.axes = tuple(qubit_map[qubit]
for qubit in op.qubits)
protocols.act_on(op, sim_state)
yield SparseSimulatorStep(
state_vector=sim_state.target_tensor,
measurements=dict(sim_state.log_of_measurement_results),
qubit_map=qubit_map,
dtype=self._dtype,
)
sim_state.log_of_measurement_results.clear()
