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mat_mat_op now also works with complex matrices

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Fabian Joswig 2021-10-17 13:03:14 +01:00
parent 15333d2629
commit 157fc1058a
1 changed files with 25 additions and 4 deletions
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23
pyerrors/linalg.py
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@ -3,7 +3,7 @@
import numpy as np
import autograd.numpy as anp # Thinly-wrapped numpy
from .pyerrors import derived_observable
from .pyerrors import derived_observable, CObs
# This code block is directly taken from the current master branch of autograd and remains
@ -84,6 +84,27 @@ def scalar_mat_op(op, obs, **kwargs):
def mat_mat_op(op, obs, **kwargs):
"""Computes the matrix to matrix operation op to a given matrix of Obs."""
# Use real representation to calculate matrix operations for complex matrices
if isinstance(obs.ravel()[0], CObs):
A = np.empty_like(obs)
B = np.empty_like(obs)
for (n, m), entry in np.ndenumerate(obs):
if hasattr(entry, 'real') and hasattr(entry, 'imag'):
A[n, m] = entry.real
B[n, m] = entry.imag
else:
A[n, m] = entry
B[n, m] = 0.0
big_matrix = np.bmat([[A, -B], [B, A]])
if kwargs.get('num_grad') is True:
op_big_matrix = _num_diff_mat_mat_op(op, big_matrix, **kwargs)
else:
op_big_matrix = derived_observable(lambda x, **kwargs: op(x), big_matrix)
dim = op_big_matrix.shape[0]
op_A = op_big_matrix[0: dim // 2, 0: dim // 2]
op_B = op_big_matrix[dim // 2:, 0: dim // 2]
return (1 + 0j) * op_A + 1j * op_B
else:
if kwargs.get('num_grad') is True:
return _num_diff_mat_mat_op(op, obs, **kwargs)
return derived_observable(lambda x, **kwargs: op(x), obs)