def test_dv_dtype(self):
D = ch.QuantumState(ch._vec(np.eye(2, dtype=np.complex128) * .5), 'dv')
self.assertEqual(type(D._dv.density_vector),
type(np.array([]))) #Changed Type
self.assertEqual(D._dv.density_vector.dtype, np.complex128)
D = ch.QuantumState(ch._vec(np.eye(2) * .5), 'dv')
self.assertEqual(type(D._dv.density_vector),
type(np.array([]))) #Changed Type
self.assertEqual(D._dv.density_vector.dtype, np.complex128)
self.assertEqual(type(D.density_vector()),
type(np.array([]))) #Changed Type
self.assertEqual(D.density_vector().dtype, np.complex128)
self.assertEqual(type(D.density_matrix()),
type(np.array([]))) #Changed Type
self.assertEqual(D.density_matrix().dtype, np.complex128)
self.assertEqual(type(D.bloch_vector()),
type(np.array([]))) #Changed Type
self.assertEqual(D.bloch_vector().dtype, np.float64)
w, b = D.state_mixture()
self.assertEqual(type(w), np.ndarray)
self.assertEqual(w.dtype, np.float64)
self.assertTrue(all([type(bb) == type(np.array([]))
for bb in b])) #Changed Type
self.assertTrue(all([bb.dtype == np.complex128 for bb in b]))
def test_kraus_to_all(self):
paulis = [ch._sigmaI, ch._sigmaX, ch._sigmaY, ch._sigmaZ]
weights = np.random.randn(4) + 1j * np.random.randn(4)
weights = weights / la.norm(weights)
kraus = [w * p for w, p in zip(weights, paulis)]
D = ch.QuantumChannel(kraus, 'kraus')
kraus2 = D.kraus()
errs = [la.norm(k1 - k2) for k1, k2 in zip(kraus, kraus2)]
self.assertEqual(np.max(errs), 0)
S = np.zeros((8, 2), dtype=np.complex128)
for i in range(4):
S[i * 2:(i + 1) * 2, :] = kraus[i]
D = ch.QuantumChannel(kraus, 'kraus')
S2 = D.stiefel()
self.assertEqual(la.norm(S - S2), 0)
S = np.zeros((8, 2), dtype=np.complex128)
for i in range(4):
S[i::4, :] = kraus[i]
D = ch.QuantumChannel(kraus, 'kraus')
S2 = D.stiefel2()
self.assertEqual(la.norm(S - S2), 0)
D = ch.QuantumChannel(kraus, 'kraus')
L = np.sum([
w.conj() * w * np.kron(p.conj(), p)
for w, p in zip(weights, paulis)
], 0)
L2 = D.liouvillian()
self.assertAlmostEqual(la.norm(L - L2), 0, 15)
ptms = [np.eye(4) for _ in paulis]
for i in range(1, 4):
for j in range(1, 4):
if i != j:
ptms[i][j, j] = -1
PTM = np.sum([w.conj() * w * p for w, p in zip(weights, ptms)], 0)
D = ch.QuantumChannel(kraus, 'kraus')
PTM2 = D.ptm()
self.assertAlmostEqual(la.norm(PTM - PTM2), 0)
choi = np.sum([
w.conj() * w * ch._vec(p) * ch._vec(p).conj().T
for w, p in zip(weights, paulis)
], 0)
D = ch.QuantumChannel(kraus, 'kraus')
choi2 = D.choi()
self.assertAlmostEqual(la.norm(choi - choi2), 0)
chi = np.diag([w.conj() * w for w in weights])
D = ch.QuantumChannel(kraus, 'kraus')
chi2 = D.chi()
self.assertAlmostEqual(la.norm(chi - chi2), 0, 15)
def test_ptm_to_all(self):
paulis = [ch._sigmaI, ch._sigmaX, ch._sigmaY, ch._sigmaZ]
weights = np.random.randn(4) + 1j * np.random.randn(4)
weights = weights / la.norm(weights)
ptms = [np.eye(4) for _ in paulis]
for i in range(1, 4):
for j in range(1, 4):
if i != j:
ptms[i][j, j] = -1
PTM = np.sum([w.conj() * w * p for w, p in zip(weights, ptms)], 0)
D = ch.QuantumChannel(PTM, 'ptm')
PTM2 = D.ptm()
self.assertEqual(la.norm(PTM - PTM2), 0)
kraus = [w * p for w, p in zip(weights, paulis)]
D = ch.QuantumChannel(PTM, 'ptm')
kraus2 = D.kraus()
#errs = [la.norm(k1-k2) for k1,k2 in zip(kraus,kraus2)]
errs = [
np.min([
la.norm(np.kron(k1.conj(), k1) - np.kron(k2.conj(), k2))
for k2 in kraus2
]) for k1 in kraus
]
self.assertAlmostEqual(la.norm(errs), 0, 12)
#Skip stiefel since it is tedious due to phase ambiguity
#S = np.zeros((8,2),dtype=np.complex128)
#for i in range(4):
# S[i*2:(i+1)*2,:] = kraus[i]
D = ch.QuantumChannel(PTM, 'ptm')
S2 = D.stiefel()
#errs = [np.min([la.norm(np.kron(k1.conj(),k1)-np.kron(k2.conj(),k2)) for k2 in kraus2]) for k1 in kraus]
#self.AssertEqual(la.norm(S-S2),0)
D = ch.QuantumChannel(PTM, 'ptm')
L = np.sum([
w.conj() * w * np.kron(p.conj(), p)
for w, p in zip(weights, paulis)
], 0)
L2 = D.liouvillian()
self.assertAlmostEqual(la.norm(L - L2), 0, 12)
choi = np.sum([
w.conj() * w * ch._vec(p) * ch._vec(p).conj().T
for w, p in zip(weights, paulis)
], 0)
D = ch.QuantumChannel(PTM, 'ptm')
choi2 = D.choi()
self.assertAlmostEqual(la.norm(choi - choi2), 0, 12)
chi = np.diag([w.conj() * w for w in weights])
D = ch.QuantumChannel(PTM, 'ptm')
chi2 = D.chi()
self.assertAlmostEqual(la.norm(chi - chi2), 0, 12)
def test_liou_to_choi(self):
paulis = [ch._sigmaI, ch._sigmaX, ch._sigmaY, ch._sigmaZ]
for i, gate in enumerate(paulis):
L = np.kron(gate.conj(), gate)
D = ch.QuantumChannel(L, 'liou')
choi = D.choi()
choi_true = ch._vec(gate) * ch._vec(gate).conj().T
self.assertEqual(la.norm(choi - choi_true), 0)
def test_dv_to_dv(self):
G = np.random.randn(2, 2) + 1j * np.random.randn(2, 2)
Q, R = la.qr(G)
d = np.random.rand(2)
d = d / np.sum(d)
Q = np.array(Q)
dm = np.dot(Q, np.dot(np.diag(d), Q.conj().T))
dv = ch._vec(dm)
p = ch.QuantumState(dv, 'dv')
err1 = la.norm(dv - p.density_vector())
self.assertEqual(err1, 0)
dm = p.density_matrix()
p2 = ch.QuantumState(dm, 'dm')
err2 = la.norm(dv - p2.density_vector())
self.assertEqual(err2, 0)
bv = p.bloch_vector()
self.assertLessEqual(la.norm(bv), 1.)
p3 = ch.QuantumState(bv, 'bv')
err3 = la.norm(dv - p3.density_vector())
self.assertAlmostEqual(err3, 0, 12)
w, b = p.state_mixture()
p4 = ch.QuantumState((w, b), 'sm')
err4 = la.norm(dv - p4.density_vector())
self.assertAlmostEqual(err4, 0, 12)
