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Work around index-limiting bug in np.einsum #687

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merged 2 commits into from
Jul 17, 2018
Merged

Work around index-limiting bug in np.einsum #687

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2b2db15
Work around index-limiting bug in np.eisnum
Strilanc Jul 16, 2018
184d47b
Merge branch 'master' into einsum-workaround
Strilanc Jul 17, 2018
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11 changes: 9 additions & 2 deletions cirq/circuits/circuit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1319,11 +1319,18 @@ def _apply_unitary_operation(state: np.ndarray,
work_indices = tuple(range(k))
data_indices = tuple(range(k, k + d))
used_data_indices = tuple(data_indices[q] for q in target_axes)
input_indices = work_indices + used_data_indices
output_indices = list(data_indices)
for w, t in zip(work_indices, target_axes):
output_indices[t] = w

return np.einsum(matrix, work_indices + used_data_indices,
all_indices = set(input_indices + data_indices + tuple(output_indices))

return np.einsum(matrix, input_indices,
state, data_indices,
output_indices,
out=out)
out=out,
# Note: this is a workaround for a bug in numpy:
# https://github.com/numpy/numpy/issues/10926
# Turning optimize on actually makes things slower.
optimize=len(all_indices) >= 26)