Fix typing complaints showing at numpy 1.23 (quantumlib#5856)

- Revert "Restrict numpy version to unblock inflight PRs from failing (quantumlib#5853)"
- Clarify wording of ValueError message
- Fix typing complaints on non-native numeric types
- Fix complaint on float.__rpow__(cirq.Operation) use -
  cirq.Operation has no `__pow__`, but the GateOperation subclass has
- Fix complaint about vstack on generic array
- clean up unused "type: ignore" comments

Resolves quantumlib#5852.

Reverts quantumlib#5853.

Author: pavoljuhas

cirq/contrib/quantum_volume/quantum_volume.py

@@ -147,7 +147,7 @@ def sample_heavy_set(
 
     results = results.agg(lambda meas: cirq.value.big_endian_bits_to_int(meas), axis=1)
     # Compute the number of outputs that are in the heavy set.
-    num_in_heavy_set = np.sum(np.in1d(results, heavy_set))
+    num_in_heavy_set = np.sum(np.in1d(results, heavy_set)).item()
 
     # Return the number of Heavy outputs over the number of valid runs.
     return num_in_heavy_set / len(results)

cirq/experiments/fidelity_estimation.py

@@ -60,7 +60,7 @@ def linear_xeb_fidelity_from_probabilities(
         Estimate of fidelity associated with an experimental realization
         of a quantum circuit.
     """
-    return hilbert_space_dimension * np.mean(probabilities) - 1
+    return hilbert_space_dimension * np.mean(probabilities).item() - 1
 
 
 def log_xeb_fidelity_from_probabilities(
@@ -175,7 +175,7 @@ def xeb_fidelity(
         ValueError: Circuit is inconsistent with qubit order or one of the
             bitstrings is inconsistent with the number of qubits.
     """
-    dim = np.prod(circuit.qid_shape(), dtype=np.int64)
+    dim = np.prod(circuit.qid_shape()).item()
 
     if isinstance(bitstrings, tuple):
         bitstrings = list(bitstrings)
@@ -190,9 +190,9 @@ def xeb_fidelity(
     if amplitudes is None:
         output_state = final_state_vector(circuit, qubit_order=qubit_order)
         output_probabilities = state_vector_to_probabilities(output_state)
-        bitstring_probabilities = output_probabilities[bitstrings]
+        bitstring_probabilities = output_probabilities[bitstrings].tolist()
     else:
-        bitstring_probabilities = np.abs([amplitudes[bitstring] for bitstring in bitstrings]) ** 2
+        bitstring_probabilities = [abs(amplitudes[bitstring]) ** 2 for bitstring in bitstrings]
     return estimator(dim, bitstring_probabilities)
 
 

cirq/experiments/purity_estimation.py

@@ -58,4 +58,4 @@ def purity_from_probabilities(
     """
     D = hilbert_space_dimension
     porter_thomas_variance = (D - 1) / (D + 1) / D**2
-    return np.var(probabilities) / porter_thomas_variance
+    return np.var(probabilities).item() / porter_thomas_variance

cirq/interop/quirk/cells/input_rotation_cells.py

@@ -150,6 +150,7 @@ def _apply_unitary_(self, args: 'cirq.ApplyUnitaryArgs'):
         control_max = np.prod([q.dimension for q in self.register], dtype=np.int64).item()
 
         for i in range(control_max):
+            assert isinstance(self.base_operation, cirq.GateOperation)
             operation = self.base_operation ** (self.exponent_sign * i / control_max)
             control_index = linalg.slice_for_qubits_equal_to(control_axes, big_endian_qureg_value=i)
             sub_args = cirq.ApplyUnitaryArgs(

cirq/linalg/decompositions.py

@@ -865,7 +865,7 @@ def kak_decomposition(
     # Recover pieces.
     a1, a0 = so4_to_magic_su2s(left.T, atol=atol, rtol=rtol, check_preconditions=False)
     b1, b0 = so4_to_magic_su2s(right.T, atol=atol, rtol=rtol, check_preconditions=False)
-    w, x, y, z = (KAK_GAMMA @ np.vstack(np.angle(d))).flatten()
+    w, x, y, z = (KAK_GAMMA @ np.angle(d).reshape(-1, 1)).flatten()
     g = np.exp(1j * w)
 
     # Canonicalize.

cirq/linalg/transformations.py

@@ -335,7 +335,7 @@ def partial_trace(tensor: np.ndarray, keep_indices: Sequence[int]) -> np.ndarray
     left_indices = [keep_map[i] if i in keep_set else i for i in range(ndim)]
     right_indices = [ndim + i if i in keep_set else i for i in left_indices]
     # TODO(#5757): remove type ignore when numpy has proper override signature.
-    return np.einsum(tensor, left_indices + right_indices)  # type: ignore
+    return np.einsum(tensor, left_indices + right_indices)
 
 
 class EntangledStateError(ValueError):

cirq/protocols/trace_distance_bound.py

@@ -106,7 +106,7 @@ def _strat_distance_from_unitary(val: Any) -> Optional[float]:
             return 0.0
         return squared**0.5
 
-    return trace_distance_from_angle_list(np.angle(np.linalg.eigvals(u)))  # type: ignore[arg-type]
+    return trace_distance_from_angle_list(np.angle(np.linalg.eigvals(u)))
 
 
 def trace_distance_from_angle_list(angle_list: Union[Sequence[float], np.ndarray]) -> float:

cirq/qis/states.py

@@ -684,7 +684,7 @@ def density_matrix_from_state_vector(
         np.conj(state_vector),
         cast(List, sum_inds.tolist()),
         indices + cast(List, sum_inds[indices].tolist()),
-    )  # type: ignore
+    )
     new_shape = np.prod([shape[i] for i in indices], dtype=np.int64)
 
     return rho.reshape((new_shape, new_shape))

cirq/sim/density_matrix_utils.py

@@ -250,14 +250,14 @@ def _validate_num_qubits(density_matrix: np.ndarray) -> int:
     """
     shape = density_matrix.shape
     half_index = len(shape) // 2
-    row_size = np.prod(shape[:half_index], dtype=np.int64) if len(shape) != 0 else 0
-    col_size = np.prod(shape[half_index:], dtype=np.int64) if len(shape) != 0 else 0
+    row_size = np.prod(shape[:half_index]).item() if shape else 0
+    col_size = np.prod(shape[half_index:]).item() if shape else 0
     if row_size != col_size:
         raise ValueError(f'Matrix was not square. Shape was {shape}')
     if row_size & (row_size - 1):
         raise ValueError(
             'Matrix could not be shaped into a square matrix with dimensions '
-            'not a power of two. Shape was {}'.format(shape)
+            'that are a power of two. Shape was {}'.format(shape)
         )
     if len(shape) > 2 and not np.allclose(shape, 2):
         raise ValueError(

cirq/sim/state_vector_simulator.py

@@ -85,7 +85,8 @@ def compute_amplitudes_sweep_iter(
         trial_result_iter = self.simulate_sweep_iter(program, params, qubit_order)
 
         yield from (
-            trial_result.final_state_vector[bitstrings] for trial_result in trial_result_iter
+            trial_result.final_state_vector[bitstrings].tolist()
+            for trial_result in trial_result_iter
         )
 
 

requirements.txt

@@ -6,7 +6,7 @@ backports.cached_property~=1.0.1; python_version < '3.8'
 duet~=0.2.7
 matplotlib~=3.0
 networkx~=2.4
-numpy >= 1.16, < 1.23
+numpy~=1.16
 pandas
 sortedcontainers~=2.0
 scipy