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Fix remaining mypy/numpy errors #5677

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Merged
CirqBot merged 4 commits into from
Jul 7, 2022
Merged

Fix remaining mypy/numpy errors #5677

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51d9b5c
Fix type mismatch in _BufferedStateVector.create argument
pavoljuhas Jul 7, 2022
77222d0
Fix type of the measurements argument to CliffordState.apply_measurement
pavoljuhas Jul 7, 2022
1067620
Fix type spec of the dtype argument to StateVectorSimulationState
pavoljuhas Jul 7, 2022
d1ac967
Merge branch 'master' into fix-mypy-numpy-errors-4
dabacon Jul 7, 2022
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2 changes: 1 addition & 1 deletion cirq-core/cirq/sim/clifford/clifford_simulator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -256,7 +256,7 @@ def apply_unitary(self, op: 'cirq.Operation'):
def apply_measurement(
self,
op: 'cirq.Operation',
measurements: Dict[str, List[np.ndarray]],
measurements: Dict[str, List[int]],
prng: np.random.RandomState,
collapse_state_vector=True,
):
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4 changes: 2 additions & 2 deletions cirq-core/cirq/sim/clifford/clifford_simulator_test.py
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Expand Up @@ -540,15 +540,15 @@ def test_invalid_apply_measurement():
state = cirq.CliffordState(qubit_map={q0: 0})
measurements = {}
with pytest.raises(TypeError, match='only supports cirq.MeasurementGate'):
_ = state.apply_measurement(cirq.H(q0), measurements, np.random.RandomState())
state.apply_measurement(cirq.H(q0), measurements, np.random.RandomState())
assert measurements == {}


def test_valid_apply_measurement():
q0 = cirq.LineQubit(0)
state = cirq.CliffordState(qubit_map={q0: 0}, initial_state=1)
measurements = {}
_ = state.apply_measurement(cirq.measure(q0), measurements, np.random.RandomState())
state.apply_measurement(cirq.measure(q0), measurements, np.random.RandomState())
assert measurements == {'q(0)': [1]}


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2 changes: 1 addition & 1 deletion cirq-core/cirq/sim/state_vector_simulation_state.py
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Expand Up @@ -53,7 +53,7 @@ def create(
initial_state: Union[np.ndarray, 'cirq.STATE_VECTOR_LIKE'] = 0,
qid_shape: Optional[Tuple[int, ...]] = None,
dtype: Optional[Type[np.complexfloating]] = None,
buffer: Optional[List[np.ndarray]] = None,
buffer: Optional[np.ndarray] = None,
):
"""Initializes the object with the inputs.

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7 changes: 5 additions & 2 deletions cirq-core/cirq/testing/consistent_act_on.py
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Expand Up @@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, Optional
from typing import Any, cast, Optional, Type

import numpy as np

Expand Down Expand Up @@ -46,12 +46,15 @@ def state_vector_has_stabilizer(state_vector: np.ndarray, stabilizer: DensePauli
"""

qubits = LineQubit.range(protocols.num_qubits(stabilizer))
complex_dtype: Type[np.complexfloating] = np.complex64
if np.issubdtype(state_vector.dtype, np.complexfloating):
complex_dtype = cast(Type[np.complexfloating], state_vector.dtype)
args = state_vector_simulation_state.StateVectorSimulationState(
available_buffer=np.empty_like(state_vector),
qubits=qubits,
prng=np.random.RandomState(),
initial_state=state_vector.copy(),
dtype=state_vector.dtype,
dtype=complex_dtype,
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@dabacon dabacon Jul 7, 2022

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in this case, since this is in the simulation direct call path, should we just do a cast here?

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@pavoljuhas pavoljuhas Jul 7, 2022

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Are we sure state_vector.dtype is complex?
If so we can make it simpler with cast.

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@dabacon dabacon Jul 7, 2022

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Oh I misread where this is, this is in the testing of consistent act_on. Think this is fine. Not sure we can assume state_vector dtype is complex, so this works.

)
protocols.act_on(stabilizer, args, qubits)
return np.allclose(args.target_tensor, state_vector)
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