def g_0n(D, Num_max, NFock):
"""
returns a D-squeezed 0-ancilla
"""
r = np.log(1 / D)
psi0 = qt.squeeze(NFock, r) * qt.basis(NFock, 0)
psi = qt.Qobj()
for n in np.arange(-Num_max, Num_max + 1):
psi += np.exp(-2 * np.pi * D**2 * n**2) * qt.displace(
NFock, n * np.sqrt(2 * np.pi)) * psi0
return psi.unit()
def test_squeeze():
"Squeezing operator"
sq = squeeze(4, 0.1 + 0.1j)
sqmatrix = np.array([[0.99500417 + 0.j, 0.00000000 + 0.j,
0.07059289 - 0.07059289j, 0.00000000 + 0.j],
[0.00000000 + 0.j, 0.98503746 + 0.j,
0.00000000 + 0.j, 0.12186303 - 0.12186303j],
[-0.07059289 - 0.07059289j, 0.00000000 + 0.j,
0.99500417 + 0.j, 0.00000000 + 0.j],
[0.00000000 + 0.j, -0.12186303 - 0.12186303j,
0.00000000 + 0.j, 0.98503746 + 0.j]])
assert_equal(np.allclose(sq.full(), sqmatrix), True)
def test_squeeze():
"Squeezing operator"
sq = squeeze(4, 0.1 + 0.1j)
sqmatrix = np.array([[0.99500417 + 0.j, 0.00000000 + 0.j,
0.07059289 - 0.07059289j, 0.00000000 + 0.j],
[0.00000000 + 0.j, 0.98503746 + 0.j,
0.00000000 + 0.j, 0.12186303 - 0.12186303j],
[-0.07059289 - 0.07059289j, 0.00000000 + 0.j,
0.99500417 + 0.j, 0.00000000 + 0.j],
[0.00000000 + 0.j, -0.12186303 - 0.12186303j,
0.00000000 + 0.j, 0.98503746 + 0.j]])
assert_equal(np.allclose(sq.full(), sqmatrix), True)
def __init__(self, N, r, alpha, fockdim):
zero = qt.Qobj()
one = qt.Qobj()
for m in range(2 * N):
phi = m * np.pi / N
D = qt.displace(fockdim, alpha * np.exp(1j * phi))
S = qt.squeeze(fockdim, r * np.exp(2j * (phi - np.pi / 2)))
blade = D * S * qt.basis(fockdim, 0)
zero += blade
one += (-1)**m * blade
zero = zero / zero.norm()
one = one / one.norm()
self._alpha = alpha
self._r = r
RotationalCode.__init__(self, zero=zero, one=one, N=N)
self._name = 'cat'
def test_squeeze():
sq = qutip.squeeze(4, 0.1 + 0.1j)
sqmatrix = np.array([[
0.99500417 + 0.j, 0.00000000 + 0.j, 0.07059289 - 0.07059289j,
0.00000000 + 0.j
],
[
0.00000000 + 0.j, 0.98503746 + 0.j,
0.00000000 + 0.j, 0.12186303 - 0.12186303j
],
[
-0.07059289 - 0.07059289j, 0.00000000 + 0.j,
0.99500417 + 0.j, 0.00000000 + 0.j
],
[
0.00000000 + 0.j, -0.12186303 - 0.12186303j,
0.00000000 + 0.j, 0.98503746 + 0.j
]])
np.testing.assert_allclose(sq.full(), sqmatrix, atol=1e-8)
def test_squeeze_type():
"Operator CSR Type: squeeze"
op = squeeze(5, 0.1j)
assert_equal(isspmatrix_csr(op.data), True)
env_kwargs = {
'control_circuit' : 'ECD_control',
'init' : 'vac',
'T' : 8,
'N' : 50}
# Params for reward function
if 1: # Fock
target_state = qt.tensor(qt.basis(2,0), qt.basis(50,4))
if 0: # squeezed
N = 70
db_val = 8
z = db_val / (20 * np.log10(np.e))
target_state = qt.tensor(qt.basis(2,0), qt.squeeze(N,z)*qt.basis(N,0))
if 0: # binomial
N = 60
pz = (qt.basis(N,0) + qt.basis(N,4))/np.sqrt(2.0)
mz = qt.basis(N,2)
py = (pz + 1j*mz)/np.sqrt(2.0)
target_state = qt.tensor(qt.basis(2,0), py)
if 0: # GKP
target_state_cav = qt.qload('E:\data\gkp_sims\PPO\ECD\GKP_state_delta_0p25')
target_state = qt.tensor(qt.basis(2,0), target_state_cav)
N = target_state_cav.dims[0][0] # 200
# reward_kwargs = {'reward_mode' : 'tomography',
def test_squeeze_type():
"Operator CSR Type: squeeze"
op = squeeze(5, 0.1j)
assert_equal(isspmatrix_csr(op.data), True)
"""
Created on Tue Feb 19 14:42:03 2019
@author: z5239621
"""
import qutip as qt
import numpy as np
import operations as ops
# Define some initail parameters
N = 10
eps = 1 + 1j
#D = qt.displace(N, 1)
S = qt.squeeze(N, eps)
vacuum = qt.basis(N)
state = S*vacuum
print("State", state)
print("<S>", ops.mean_homodyne(state, np.pi/2))
print("<S^2>", ops.var_homodyne(state, 0))
print("<S^2>", ops.var_homodyne(state, np.pi/2))
print(np.exp(-2*np.abs(eps))*N)
def test_squeezing():
squeeze = qutip.squeeze(4, 0.1 + 0.1j)
a = qutip.destroy(4)
squeezing = qutip.squeezing(a, a, 0.1 + 0.1j)
assert squeeze == squeezing
Created on Mon Apr 1 14:48:35 2019
@author: Eduardo Villasenor
"""
import qutip as qt
import matplotlib.pyplot as plt
import numpy as np
r = 3
print("Mean photon number:", np.sinh(r)**2)
# N = 10
N = 10
a = qt.basis(N, 0)
Sa = qt.squeeze(N, r)
# N = 20
N = 20
b = qt.basis(N, 0)
Sb = qt.squeeze(N, r)
# N = 30
N = 50
c = qt.basis(N, 0)
Sc = qt.squeeze(N, r)
fig, axes = plt.subplots(1, 3, figsize=(12, 3))
qt.plot_fock_distribution(Sa * a,
fig=fig,
ax=axes[0],
