def tf_propagation_lind(h0, hks, col_ops, cflds_t, dt, history=False):
col_ops = tf.cast(col_ops, dtype=tf.complex128)
dt = tf.cast(dt, dtype=tf.complex128)
if hks is not None and cflds_t is not None:
cflds_t = tf.cast(cflds_t, dtype=tf.complex128)
hks = tf.cast(hks, dtype=tf.complex128)
cflds = tf.expand_dims(tf.expand_dims(cflds_t, 2), 3)
hks = tf.expand_dims(hks, 1)
h0 = tf.expand_dims(h0, 0)
prod = cflds * hks
h = h0 + tf.reduce_sum(prod, axis=0)
else:
h = h0
h_id = tf.eye(h.shape[-1], batch_shape=[h.shape[0]], dtype=tf.complex128)
l_s = tf_kron(h, h_id)
r_s = tf_kron(h_id, tf.linalg.matrix_transpose(h))
lind_op = -1j * (l_s - r_s)
col_ops_id = tf.eye(
col_ops.shape[-1], batch_shape=[col_ops.shape[0]], dtype=tf.complex128
)
l_col_ops = tf_kron(col_ops, col_ops_id)
r_col_ops = tf_kron(col_ops_id, tf.linalg.matrix_transpose(col_ops))
super_clp = tf.matmul(l_col_ops, r_col_ops, adjoint_b=True)
anticom_L_clp = 0.5 * tf.matmul(l_col_ops, l_col_ops, adjoint_a=True)
anticom_R_clp = 0.5 * tf.matmul(r_col_ops, r_col_ops, adjoint_b=True)
clp = tf.expand_dims(
tf.reduce_sum(super_clp - anticom_L_clp - anticom_R_clp, axis=0), 0
)
lind_op += clp
dU = tf.linalg.expm(lind_op * dt)
return dU
def confuse(self, pops):
"""
Apply the confusion (or misclassification) matrix to populations.
Parameters
----------
pops : list
Populations
Returns
-------
list
Populations after misclassification.
"""
conf_matrix = tf.Variable([[1]], dtype=tf.float64)
for conf_row in self.params.values():
row1 = conf_row.get_value()
row2 = tf.ones_like(row1) - row1
conf_mat = tf.concat([[row1], [row2]], 0)
conf_matrix = tf_utils.tf_kron(conf_matrix, conf_mat)
pops = tf.linalg.matmul(conf_matrix, pops)
return pops
def test_tf_kron():
for el in data["tf_kron"]:
np.testing.assert_allclose(actual=tf_kron(*el["in"]),
desired=el["desired"])