def squeezer_matrix(r, theta, D, batched=False):
"""creates the single mode squeeze matrix"""
r = tf.cast(r, tf.float64)
if not batched:
r = tf.expand_dims(r, 0) # introduce artificial batch dimension
r = tf.reshape(r, [-1, 1, 1, 1])
theta = tf.cast(theta, def_type)
theta = tf.reshape(theta, [-1, 1, 1, 1])
rng = np.arange(D)
n = np.reshape(rng, [-1, D, 1, 1])
m = np.reshape(rng, [-1, 1, D, 1])
k = np.reshape(rng, [-1, 1, 1, D])
phase = tf.exp(1j * theta * (n - m) / 2)
signs = squeeze_parity(D).reshape([1, D, 1, D])
mask = np.logical_and((m + n) % 2 == 0, k <= np.minimum(
m, n)) # kills off terms where the sum index k goes past min(m,n)
k_terms = signs * \
tf.pow(tf.sinh(r) / 2, mask * (n + m - 2 * k) / 2) * mask / \
tf.pow(tf.cosh(r), (n + m + 1) / 2) * \
tf.exp(0.5 * tf.lgamma(tf.cast(m + 1, tf.float64)) + \
0.5 * tf.lgamma(tf.cast(n + 1, tf.float64)) - \
tf.lgamma(tf.cast(k + 1, tf.float64)) -
tf.lgamma(tf.cast((m - k) / 2 + 1, tf.float64)) - \
tf.lgamma(tf.cast((n - k) / 2 + 1, tf.float64))
)
output = tf.reduce_sum(phase * tf.cast(k_terms, def_type), axis=-1)
if not batched:
# remove extra batch dimension
output = tf.squeeze(output, 0)
return output
def test_squeeze_parity(self):
self.logTestName()
parity = so.squeeze_parity(8)
self.assertAllAlmostEqual(parity, squeeze_parity_8, delta=self.tol)
def test_squeeze_parity(self, tol):
"""Test the squeeze parity function returns the correct result"""
parity = so.squeeze_parity(8)
assert np.allclose(parity, SQUEEZE_PARITY_8, atol=tol, rtol=0)