def single_squeezing_matrix(r, phi, cutoff, dtype=tf.complex64.as_numpy_dtype):
"""creates a single-mode squeezing matrix"""
r = r.numpy()
phi = phi.numpy()
gate = squeezing_tw(r, phi, cutoff, dtype)
# NOTE: tested independently in thewalrus
def grad(dy): # pragma: no cover
Dr, Dphi = grad_squeezing_tw(gate, r, phi)
grad_r = tf.math.real(tf.reduce_sum(dy * tf.math.conj(Dr)))
grad_phi = tf.math.real(tf.reduce_sum(dy * tf.math.conj(Dphi)))
return grad_r, grad_phi, None
return gate, grad
def squeezing(r, theta, trunc):
r"""The squeezing operator :math:`S(re^{i\theta})`.
Uses the `squeezing operation from The Walrus`_ to calculate the squeezing.
.. _`squeezing operation from The Walrus`: https://the-walrus.readthedocs.io/en/latest/code/api/thewalrus.fock_gradients.squeezing.html
Args:
r (float): the magnitude of the squeezing
theta (float): the squeezing angle
trunc (int): the Fock cutoff
"""
ret = squeezing_tw(r, theta, cutoff=trunc)
return ret