def test_1d_fit(self):
D = {'I': ch.QuantumChannel(np.array(np.eye(4, dtype=complex)))}
MA = SchWARMA.SchWMA([], [s_z], D)
alpha = np.sqrt(.00001)
sigma = 1. #1./2.
arg = lambda t: alpha**2 * sigma * (np.sqrt(np.pi) * t * sp.erf(
t / sigma) + sigma * (np.exp(-t**2 / sigma**2) - 1))
args = np.exp(-2 * np.array([arg(t) for t in range(100)]))
args = (1. - args) / 2.
MA.fit([args], 5)
self.assertEqual(5, len(MA.models[0].b))
self.assertEqual(0, len(MA.models[0].a))
MA.init_ARMAs()
MA['I']
#Note now I have brackets on the second argument
MA.fit([args], [7])
self.assertEqual(7, len(MA.models[0].b))
self.assertEqual(0, len(MA.models[0].a))
MA.init_ARMAs()
L = MA['I'].liouvillian()
#Known structure of \sigma_z arma
self.assertEqual(la.norm(L - np.diag(np.diag(L))), 0.0)
self.assertEqual(L[0, 0], L[3, 3])
self.assertAlmostEqual(L[0, 0], 1., 9)
self.assertEqual(L[1, 1], np.conj(L[2, 2]))
def test_init(self):
ARMAS = [
SchWARMA.ARMA([],
np.random.randn(np.random.randint(5)) + 2)
for _ in range(3)
]
D = {'I': ch.QuantumChannel(np.array(np.eye(4, dtype=complex)))}
MA = SchWARMA.SchWMA(ARMAS, [s_x, s_y, s_z], D)
def test_3d_fit(self):
D = {'I': ch.QuantumChannel(np.array(np.eye(4, dtype=complex)))}
MA = SchWARMA.SchWMA([], [s_z], D)
alpha = np.sqrt(.00001)
sigma = 1. #1./2.
arg = lambda t: alpha**2 * sigma * (np.sqrt(np.pi) * t * sp.erf(
t / sigma) + sigma * (np.exp(-t**2 / sigma**2) - 1))
args = np.exp(-2 * np.array([arg(t) for t in range(100)]))
args = (1. - args) / 2.
#Note now I have brackets on the second argument
MA.fit([args, args, args], [3, 5, 7])
self.assertEqual(3, len(MA.models[0].b))
self.assertEqual(5, len(MA.models[1].b))
self.assertEqual(7, len(MA.models[2].b))
MA.init_ARMAs()
L = MA['I']
def test_avg(self):
D = {'I': ch.QuantumChannel(np.array(np.eye(4, dtype=complex)))}
MA = SchWARMA.SchWMA([], [s_z], D)
alpha = np.sqrt(.00001)
sigma = 1. #1./2.
arg = lambda t: alpha**2 * sigma * (np.sqrt(np.pi) * t * sp.erf(
t / sigma) + sigma * (np.exp(-t**2 / sigma**2) - 1))
args = np.exp(-2 * np.array([arg(t) for t in range(100)]))
#args = (1.-args)/2.
MA.fit([(1. - args) / 2.], 5)
self.assertEqual(5, len(MA.models[0].b))
self.assertEqual(0, len(MA.models[0].a))
MA.init_ARMAs()
MA['I']
L1 = MA.avg(1,
[ch.QuantumChannel(s_z, 'unitary').choi()]).liouvillian()
tL1 = np.array(np.eye(4, dtype=complex))
tL1[1, 1] = args[1]
tL1[2, 2] = args[1]
self.assertAlmostEqual(la.norm(L1 - tL1), 0.0, 6)
L2 = MA.avg(10,
[ch.QuantumChannel(s_z, 'unitary').choi()]).liouvillian()
tL2 = np.eye(4, dtype=complex)
tL2[1, 1] = args[10]
tL2[2, 2] = args[10]
self.assertAlmostEqual(la.norm(L2 - tL2), 0.0, 5)
#Not a great test, but should be true
self.assertLess(la.norm(L2 - tL2), la.norm(tL1**10 - L2))