def test_grad_squeezing():
"""Tests the value of the analytic gradient for the Sgate against finite differences"""
cutoff = 4
r = 1.0
theta = np.pi / 8
T = squeezing(r, theta, cutoff)
Dr, Dtheta = grad_squeezing(T, r, theta)
dr = 0.001
dtheta = 0.001
Drp = squeezing((r + dr), theta, cutoff)
Drm = squeezing((r - dr), theta, cutoff)
Dthetap = squeezing(r, theta + dtheta, cutoff)
Dthetam = squeezing(r, theta - dtheta, cutoff)
Drapprox = (Drp - Drm) / (2 * dr)
Dthetaapprox = (Dthetap - Dthetam) / (2 * dtheta)
assert np.allclose(Dr, Drapprox, atol=1e-5, rtol=0)
assert np.allclose(Dtheta, Dthetaapprox, atol=1e-5, rtol=0)
def test_squeezing_values(tol):
"""Tests the correct construction of the single mode squeezing operation"""
r = 0.5
theta = 0.7
cutoff = 5
# This data is obtained by using qutip
# np.array(squeeze(40,0.5*np.exp(1j*0.7)).data.todense())[0:5,0:5]
expected = np.array([
[
0.94171062 + 0.0j,
0.0 + 0.0j,
0.23535661 - 0.19823814j,
0.0 + 0.0j,
0.02093159 - 0.12135894j,
],
[
0.0 + 0.0j, 0.83512676 + 0.0j, 0.0 + 0.0j,
0.36151137 - 0.30449682j, 0.0 + 0.0j
],
[
-0.23535661 - 0.19823814j,
0.0 + 0.0j,
0.64005396 + 0.0j,
0.0 + 0.0j,
0.42261153 - 0.35596078j,
],
[
0.0 + 0.0j, -0.36151137 - 0.30449682j, 0.0 + 0.0j,
0.38926873 + 0.0j, 0.0 + 0.0j
],
[
0.02093159 + 0.12135894j,
0.0 + 0.0j,
-0.42261153 - 0.35596078j,
0.0 + 0.0j,
0.12407853 + 0.0j,
],
])
T = squeezing(r, theta, cutoff)
assert np.allclose(T, expected, atol=tol, rtol=0)