def test_grad_displacement():
"""Tests the value of the analytic gradient for the Dgate against finite differences"""
cutoff = 4
r = 1.0
theta = np.pi / 8
T = displacement(r, theta, cutoff)
Dr, Dtheta = grad_displacement(T, r, theta)
dr = 0.001
dtheta = 0.001
Drp = displacement(r + dr, theta, cutoff)
Drm = displacement(r - dr, theta, cutoff)
Dthetap = displacement(r, theta + dtheta, cutoff)
Dthetam = displacement(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_displacement_values(tol):
"""Tests the correct construction of the single mode displacement operation"""
cutoff = 5
alpha = 0.3 + 0.5 * 1j
# This data is obtained by using qutip
# np.array(displace(40,alpha).data.todense())[0:5,0:5]
expected = np.array(
[
[
0.84366482 + 0.00000000e00j,
-0.25309944 + 4.21832408e-01j,
-0.09544978 - 1.78968334e-01j,
0.06819609 + 3.44424719e-03j,
-0.01109048 + 1.65323865e-02j,
],
[
0.25309944 + 4.21832408e-01j,
0.55681878 + 0.00000000e00j,
-0.29708743 + 4.95145724e-01j,
-0.14658716 - 2.74850926e-01j,
0.12479885 + 6.30297236e-03j,
],
[
-0.09544978 + 1.78968334e-01j,
0.29708743 + 4.95145724e-01j,
0.31873657 + 0.00000000e00j,
-0.29777767 + 4.96296112e-01j,
-0.18306015 - 3.43237787e-01j,
],
[
-0.06819609 + 3.44424719e-03j,
-0.14658716 + 2.74850926e-01j,
0.29777767 + 4.96296112e-01j,
0.12389162 + 1.10385981e-17j,
-0.27646677 + 4.60777945e-01j,
],
[
-0.01109048 - 1.65323865e-02j,
-0.12479885 + 6.30297236e-03j,
-0.18306015 + 3.43237787e-01j,
0.27646677 + 4.60777945e-01j,
-0.03277289 + 1.88440656e-17j,
],
]
)
T = displacement(np.abs(alpha), np.angle(alpha), cutoff)
assert np.allclose(T, expected, atol=tol, rtol=0)