def test_calculate_decay_amplitudes(self):
"""Test raises of numeric.calculate_decay_amplitudes"""
pulse = testutil.rand_pulse_sequence(2, 1, 1, 1)
omega = rng.standard_normal(43)
# single spectrum
spectrum = rng.standard_normal(78)
for i in range(4):
with self.assertRaises(ValueError):
numeric.calculate_decay_amplitudes(pulse, np.tile(spectrum, [1]*i), omega)
def test_calculate_error_vector_correlation_functions(self):
"""Test raises of numeric.error_transfer_matrix"""
pulse = testutil.rand_pulse_sequence(2, 1, 1, 1)
omega = rng.standard_normal(43)
# single spectrum
S = rng.standard_normal(78)
for i in range(4):
with self.assertRaises(ValueError):
numeric.calculate_error_vector_correlation_functions(
pulse, np.tile(S, [1] * i), omega)
def test_oper_equiv(self):
with self.assertRaises(ValueError):
util.oper_equiv(rng.standard_normal((2, 2)),
rng.standard_normal((3, 3)))
for d in rng.randint(2, 10, (5, )):
psi = rng.standard_normal((d, 1)) + 1j * rng.standard_normal(
(d, 1))
# Also test broadcasting
U = testutil.rand_herm(d, rng.randint(1, 11)).squeeze()
phase = rng.standard_normal()
result = util.oper_equiv(psi, psi * np.exp(1j * phase))
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], phase, places=5)
result = util.oper_equiv(psi * np.exp(1j * phase), psi)
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], -phase, places=5)
psi /= np.linalg.norm(psi, ord=2)
result = util.oper_equiv(psi,
psi * np.exp(1j * phase),
normalized=True,
eps=1e-13)
self.assertTrue(result[0])
self.assertArrayAlmostEqual(result[1], phase, atol=1e-5)
result = util.oper_equiv(psi, psi + 1)
self.assertFalse(result[0])
result = util.oper_equiv(U, U * np.exp(1j * phase))
self.assertTrue(np.all(result[0]))
self.assertArrayAlmostEqual(result[1], phase, atol=1e-5)
result = util.oper_equiv(U * np.exp(1j * phase), U)
self.assertTrue(np.all(result[0]))
self.assertArrayAlmostEqual(result[1], -phase, atol=1e-5)
norm = np.sqrt(util.dot_HS(U, U))
norm = norm[:, None, None] if U.ndim == 3 else norm
U /= norm
# TIP: In numpy 1.18 we could just do:
# U /= np.expand_dims(np.sqrt(util.dot_HS(U, U)), axis=(-1, -2))
result = util.oper_equiv(U,
U * np.exp(1j * phase),
normalized=True,
eps=1e-10)
self.assertTrue(np.all(result[0]))
self.assertArrayAlmostEqual(result[1], phase)
result = util.oper_equiv(U, U + 1)
self.assertFalse(np.all(result[0]))
def test_integrate(self):
f = rng.standard_normal(32)
x = rng.random(32)
self.assertEqual(util.integrate(f, x), np.trapz(f, x))
f = rng.standard_normal((2, 32)).astype(complex)
x = rng.random(32)
self.assertArrayEqual(util.integrate(f, x), np.trapz(f, x))
f = rng.standard_normal(32)
x = np.linspace(0, 1, 32)
dx = 1 / 31
self.assertAlmostEqual(util.integrate(f, x), util.integrate(f, dx=dx))
def test_calculate_cumulant_function(self):
"""Test numeric.calculate_cumulant_function"""
pulse = testutil.rand_pulse_sequence(2, 1, 1, 1)
omega = rng.standard_normal(43)
# single spectrum
spectrum = rng.standard_normal(43)
Gamma = numeric.calculate_decay_amplitudes(pulse, spectrum, omega)
K_1 = numeric.calculate_cumulant_function(pulse, spectrum, omega)
K_2 = numeric.calculate_cumulant_function(pulse, decay_amplitudes=Gamma)
self.assertArrayAlmostEqual(K_1, K_2)
with self.assertRaises(ValueError):
numeric.calculate_cumulant_function(pulse, None, None, None)
def test_basis_expansion_and_normalization(self):
"""Correct expansion of operators and normalization of bases"""
for _ in range(10):
d = rng.randint(2, 16)
ggm_basis = ff.Basis.ggm(d)
basis = ff.Basis(np.einsum('i,ijk->ijk', rng.standard_normal(d**2),
ggm_basis),
skip_check=True)
M = rng.standard_normal((d, d)) + 1j * rng.standard_normal((d, d))
M -= np.trace(M) / d
coeffs = ff.basis.expand(M, basis, normalized=False)
self.assertArrayAlmostEqual(M, np.einsum('i,ijk', coeffs, basis))
self.assertArrayAlmostEqual(ff.basis.expand(M, ggm_basis),
ff.basis.ggm_expand(M),
atol=1e-14)
self.assertArrayAlmostEqual(ff.basis.ggm_expand(M),
ff.basis.ggm_expand(M, traceless=True),
atol=1e-14)
n = rng.randint(1, 50)
M = (rng.standard_normal((n, d, d)) + 1j * rng.standard_normal(
(n, d, d)))
coeffs = ff.basis.expand(M, basis, normalized=False)
self.assertArrayAlmostEqual(
M, np.einsum('li,ijk->ljk', coeffs, basis))
self.assertArrayAlmostEqual(ff.basis.expand(M, ggm_basis),
ff.basis.ggm_expand(M),
atol=1e-14)
# Argument to ggm_expand not square in last two dimensions
with self.assertRaises(ValueError):
ff.basis.ggm_expand(basis[..., 0])
self.assertTrue(ff.basis.normalize(basis).isorthonorm)
# Basis method and function should give the same
normalized = ff.basis.normalize(basis)
basis.normalize()
self.assertEqual(normalized, basis)
# normalize single element
elem = basis[1]
normalized = ff.basis.normalize(elem)
elem.normalize()
self.assertEqual(normalized, elem)
# Not matrix or sequence of matrices
with self.assertRaises(ValueError):
ff.basis.normalize(basis[0, 0])
def test_oper_equiv(self):
with self.assertRaises(ValueError):
util.oper_equiv(*[np.ones((1, 2, 3))] * 2)
for d in rng.randint(2, 10, (5, )):
psi = rng.standard_normal((d, 1)) + 1j * rng.standard_normal(
(d, 1))
U = testutil.rand_herm(d).squeeze()
phase = rng.standard_normal()
result = util.oper_equiv(psi, psi * np.exp(1j * phase))
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], phase, places=5)
result = util.oper_equiv(psi * np.exp(1j * phase), psi)
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], -phase, places=5)
psi /= np.linalg.norm(psi, ord=2)
result = util.oper_equiv(psi,
psi * np.exp(1j * phase),
normalized=True,
eps=1e-13)
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], phase, places=5)
result = util.oper_equiv(psi, psi + 1)
self.assertFalse(result[0])
result = util.oper_equiv(U, U * np.exp(1j * phase))
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], phase, places=5)
result = util.oper_equiv(U * np.exp(1j * phase), U)
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], -phase, places=5)
U /= np.sqrt(util.dot_HS(U, U))
result = util.oper_equiv(U,
U * np.exp(1j * phase),
normalized=True,
eps=1e-10)
self.assertTrue(result[0])
self.assertAlmostEqual(result[1], phase)
result = util.oper_equiv(U, U + 1)
self.assertFalse(result[0])
def test_get_sample_frequencies(self):
pulse = PulseSequence([[util.paulis[1], [np.pi / 2]]],
[[util.paulis[1], [1]]],
[abs(rng.standard_normal())])
# Default args
omega = util.get_sample_frequencies(pulse)
self.assertAlmostEqual(omega[0], 2e-2 * np.pi / pulse.tau)
self.assertAlmostEqual(omega[-1], 2e2 * np.pi / pulse.tau)
self.assertEqual(len(omega), 300)
self.assertTrue((omega >= 0).all())
self.assertLessEqual(np.var(np.diff(np.log(omega[150:]))), 1e-16)
# custom args
omega = util.get_sample_frequencies(pulse,
spacing='linear',
n_samples=50,
include_quasistatic=True)
self.assertAlmostEqual(omega[0], 0)
self.assertAlmostEqual(omega[-1], 2e2 * np.pi / pulse.tau)
self.assertEqual(len(omega), 50)
self.assertTrue((omega >= 0).all())
self.assertLessEqual(np.var(np.diff(omega[1:])), 1e-16)
# Exceptions
with self.assertRaises(ValueError):
omega = util.get_sample_frequencies(pulse, spacing='foo')
def test_basis_constructor(self):
"""Test the constructor for several failure modes"""
# Constructing from given elements should check for __getitem__
with self.assertRaises(TypeError):
_ = ff.Basis(1)
# All elements should be either sparse, Qobj, or ndarray
elems = [
ff.util.paulis[1],
COO.from_numpy(ff.util.paulis[3]), [[0, 1], [1, 0]]
]
with self.assertRaises(TypeError):
_ = ff.Basis(elems)
# Too many elements
with self.assertRaises(ValueError):
_ = ff.Basis(rng.standard_normal((5, 2, 2)))
# Properly normalized
self.assertEqual(ff.Basis.pauli(1), ff.Basis(ff.util.paulis))
# Non traceless elems but traceless basis requested
with self.assertRaises(ValueError):
_ = ff.Basis(np.ones((2, 2)), traceless=True)
# Calling with only the identity should work with traceless true or
# false
self.assertEqual(ff.Basis(np.eye(2), traceless=False),
ff.Basis(np.eye(2), traceless=True))
# Constructing a basis from a basis should work
_ = ff.Basis(ff.Basis.ggm(2)[1:])
def test_oper_equiv(self):
self.assertFalse(
util.oper_equiv(qutip.rand_ket(2), qutip.rand_dm(2))[0])
for d in rng.integers(2, 10, (5, )):
psi = qutip.rand_ket(d)
U = qutip.rand_dm(d)
phase = rng.standard_normal()
result = util.oper_equiv(psi, psi * np.exp(1j * phase))
self.assertTrue(result[0])
self.assertAlmostEqual(np.mod(result[1], 2 * np.pi),
np.mod(phase, 2 * np.pi),
places=5)
result = util.oper_equiv(psi * np.exp(1j * phase), psi)
self.assertTrue(result[0])
self.assertAlmostEqual(np.mod(result[1], 2 * np.pi),
np.mod(-phase, 2 * np.pi),
places=5)
result = util.oper_equiv(U, U * np.exp(1j * phase))
self.assertTrue(result[0])
self.assertAlmostEqual(np.mod(result[1], 2 * np.pi),
np.mod(phase, 2 * np.pi),
places=5)
result = util.oper_equiv(U * np.exp(1j * phase), U)
self.assertTrue(result[0])
self.assertAlmostEqual(np.mod(result[1], 2 * np.pi),
np.mod(-phase, 2 * np.pi),
places=5)
def test_SE(self):
"""Spin echo"""
tau = np.pi
tau_pi = 1e-8
n = 1
H_c, dt = testutil.generate_dd_hamiltonian(n,
tau=tau,
tau_pi=tau_pi,
dd_type='cpmg')
H_n = [[util.paulis[3] / 2, np.ones_like(dt)]]
SE_pulse = ff.PulseSequence(H_c, H_n, dt)
omega = util.get_sample_frequencies(SE_pulse, 100, spacing='linear')
# Comparison to filter function defined with omega**2
F = SE_pulse.get_filter_function(omega)[0, 0] * omega**2
self.assertArrayAlmostEqual(F, analytic.SE(omega * tau), atol=1e-10)
# Test again with a factor of one between the noise operators and
# coefficients
r = rng.standard_normal()
H_n = [[util.paulis[3] / 2 * r, np.ones_like(dt) / r]]
SE_pulse = ff.PulseSequence(H_c, H_n, dt)
# Comparison to filter function defined with omega**2
F = SE_pulse.get_filter_function(omega)[0, 0] * omega**2
self.assertArrayAlmostEqual(F, analytic.SE(omega * tau), atol=1e-10)
def test_tensor_transpose(self):
# Test basic functionality
paulis = np.array(util.paulis)
I, X, Y, Z = paulis
arr = util.tensor(I, X, Y, Z)
arr_dims = [[2] * 4] * 2
order = np.arange(4)
for _ in range(20):
order = rng.permutation(order)
r = util.tensor_transpose(arr, order, arr_dims)
self.assertArrayAlmostEqual(r, util.tensor(*paulis[order]))
# Check exceptions
with self.assertRaises(ValueError):
# wrong arr_dims (too few dims)
r = util.tensor_transpose(arr, order, [[2] * 3] * 2)
with self.assertRaises(ValueError):
# wrong arr_dims (too few dims)
r = util.tensor_transpose(arr, order, [[2] * 4] * 1)
with self.assertRaises(ValueError):
# wrong arr_dims (dims too large)
r = util.tensor_transpose(arr, order, [[3] * 4] * 2)
with self.assertRaises(ValueError):
# wrong order (too few axes)
r = util.tensor_transpose(arr, (0, 1, 2), arr_dims)
with self.assertRaises(ValueError):
# wrong order (index 4 too large)
r = util.tensor_transpose(arr, (1, 2, 3, 4), arr_dims)
with self.assertRaises(ValueError):
# wrong order (not unique axes)
r = util.tensor_transpose(arr, (1, 1, 1, 1), arr_dims)
with self.assertRaises(TypeError):
# wrong order (floats instead of ints)
r = util.tensor_transpose(arr, (0., 1., 2., 3.), arr_dims)
# random tests
for rank, n_args, n_broadcast in zip(rng.randint(1, 4, 10),
rng.randint(3, 6, 10),
rng.randint(1, 11, 10)):
arrs = rng.standard_normal((n_args, n_broadcast, *[2] * rank))
order = rng.permutation(n_args)
arr_dims = [[2] * n_args] * rank
r = util.tensor_transpose(util.tensor(*arrs, rank=rank),
order=order,
arr_dims=arr_dims,
rank=rank)
self.assertArrayAlmostEqual(r, util.tensor(*arrs[order],
rank=rank))
def test_cexp(self):
"""Fast complex exponential."""
x = rng.standard_normal((50, 100))
a = util.cexp(x)
b = np.exp(1j * x)
self.assertArrayAlmostEqual(a, b)
a = util.cexp(-x)
b = np.exp(-1j * x)
self.assertArrayAlmostEqual(a, b)
def test_FID(self):
"""FID"""
tau = abs(rng.standard_normal())
FID_pulse = ff.PulseSequence([[util.paulis[1] / 2, [0]]],
[[util.paulis[3] / 2, [1]]], [tau])
omega = util.get_sample_frequencies(FID_pulse, 50, spacing='linear')
# Comparison to filter function defined with omega**2
F = FID_pulse.get_filter_function(omega).squeeze() * omega**2
self.assertArrayAlmostEqual(F, analytic.FID(omega * tau), atol=1e-10)
def test_copy(self):
pulse = testutil.rand_pulse_sequence(2, 2)
old_copers = pulse.c_opers.copy()
copied = copy.copy(pulse)
deepcopied = copy.deepcopy(pulse)
self.assertEqual(pulse, copied)
self.assertEqual(pulse, deepcopied)
pulse.c_opers[...] = rng.standard_normal(size=pulse.c_opers.shape)
self.assertArrayEqual(pulse.c_opers, copied.c_opers)
self.assertArrayEqual(old_copers, deepcopied.c_opers)
self.assertEqual(pulse, copied)
self.assertNotEqual(pulse, deepcopied)
def test_symmetrize_spectrum(self):
pulse = PulseSequence(
[[util.paulis[1], [np.pi/2]]],
[[util.paulis[1], [1]]],
[abs(rng.standard_normal())]
)
asym_omega = util.get_sample_frequencies(pulse, symmetric=False,
n_samples=100)
sym_omega = util.get_sample_frequencies(pulse, symmetric=True,
n_samples=200)
S_symmetrized, omega_symmetrized = util.symmetrize_spectrum(
1/asym_omega**0.7, asym_omega)
self.assertArrayEqual(omega_symmetrized, sym_omega)
self.assertArrayEqual(S_symmetrized[99::-1], S_symmetrized[100:])
self.assertArrayEqual(S_symmetrized[100:]*2, 1/asym_omega**0.7)
# zero frequency not doubled
omega = np.arange(10)
S_sym, omega_sym = util.symmetrize_spectrum(omega, omega)
self.assertArrayEqual(S_sym, np.abs(np.arange(-9, 10)/2))
self.assertArrayEqual(omega_sym, np.arange(-9, 10))
def test_tensor(self):
shapes = [(1, 2, 3, 4, 5), (5, 4, 3, 2, 1)]
A = rng.standard_normal(shapes[0])
B = rng.standard_normal(shapes[1])
with self.assertRaises(ValueError):
util.tensor(A, B)
shapes = [(3, 2, 1), (3, 4, 2)]
A = rng.standard_normal(shapes[0])
B = rng.standard_normal(shapes[1])
with self.assertRaises(ValueError):
util.tensor(A, B, rank=1)
self.assertEqual(util.tensor(A, B, rank=2).shape, (3, 8, 2))
self.assertEqual(util.tensor(A, B, rank=3).shape, (9, 8, 2))
shapes = [(10, 1, 3, 2), (10, 1, 2, 3)]
A = rng.standard_normal(shapes[0])
B = rng.standard_normal(shapes[1])
self.assertEqual(util.tensor(A, B).shape, (10, 1, 6, 6))
shapes = [(3, 5, 4, 4), (3, 5, 4, 4)]
A = rng.standard_normal(shapes[0])
B = rng.standard_normal(shapes[1])
self.assertEqual(util.tensor(A, B).shape, (3, 5, 16, 16))
d = rng.randint(2, 9)
eye = np.eye(d)
for i in range(d):
for j in range(d):
A, B = eye[i:i + 1, :], eye[:, j:j + 1]
self.assertArrayEqual(util.tensor(A, B, rank=1), np.kron(A, B))
i, j = rng.randint(0, 4, (2, ))
A, B = util.paulis[i], util.paulis[j]
self.assertArrayEqual(util.tensor(A, B), np.kron(A, B))
args = [
rng.standard_normal((4, 1, 2)),
rng.standard_normal((3, 2)),
rng.standard_normal((4, 3, 5))
]
self.assertEqual(util.tensor(*args, rank=1).shape, (4, 3, 20))
self.assertEqual(util.tensor(*args, rank=2).shape, (4, 9, 20))
self.assertEqual(util.tensor(*args, rank=3).shape, (16, 9, 20))
args = [rng.standard_normal((2, 3, 4)), rng.standard_normal((4, 3))]
with self.assertRaises(ValueError) as err:
util.tensor(*args, rank=1)
msg = ('Incompatible shapes (2, 3, 4) and (4, 3) for tensor ' +
'product of rank 1.')
self.assertEqual(msg, str(err.exception))
def test_infidelity(self):
"""Benchmark infidelity results against previous version's results"""
rng = np.random.default_rng(seed=123456789)
spectra = [
lambda S0, omega: S0 * abs(omega)**0,
lambda S0, omega: S0 / abs(omega)**0.7,
lambda S0, omega: S0 * np.exp(-abs(omega)),
# different spectra for different n_opers
lambda S0, omega: np.array(
[S0 * abs(omega)**0, S0 / abs(omega)**0.7]),
# cross-correlated spectra
lambda S0, omega: np.array([[
S0 / abs(omega)**0.7, S0 / (1 + omega**2) + 1j * S0 * omega
], [S0 / (1 + omega**2) - 1j * S0 * omega, S0 / abs(omega)**0.7]])
]
ref_infids = ([[2.1571674053883583, 2.1235628100639845],
[1.7951695420688032, 2.919850951578396],
[0.4327173760925169, 0.817672660809546],
[2.1571674053883583, 2.919850951578396],
[[1.7951695420688032, -1.1595479985471822],
[-1.1595479985471822, 2.919850951578396]],
[0.8247284959004152, 2.495561429509174],
[0.854760904366362, 3.781670732974073],
[0.24181791977082442, 1.122626106375816],
[0.8247284959004152, 3.781670732974073],
[[0.854760904366362, -0.16574972846239408],
[-0.16574972846239408, 3.781670732974073]],
[2.9464977186365267, 0.8622319594213088],
[2.8391133843027525, 0.678843575761492],
[0.813728718501677, 0.16950739577216872],
[2.9464977186365267, 0.678843575761492],
[[2.8391133843027525, 0.2725782717379744],
[0.2725782717379744, 0.678843575761492]]])
count = 0
for d in (2, 3, 4):
pulse = testutil.rand_pulse_sequence(d, 10, 2, 3, local_rng=rng)
pulse.n_oper_identifiers = np.array(['B_0', 'B_2'])
omega = np.geomspace(0.1, 10, 51)
S0 = np.abs(rng.standard_normal())
for spec in spectra:
S = spec(S0, omega)
infids = ff.infidelity(pulse,
S,
omega,
n_oper_identifiers=['B_0', 'B_2'])
self.assertArrayAlmostEqual(infids,
ref_infids[count],
atol=1e-12)
if S.ndim == 3:
# Diagonal of the infidelity matrix should correspond to
# uncorrelated terms
uncorrelated_infids = ff.infidelity(
pulse,
S[range(2), range(2)],
omega,
n_oper_identifiers=['B_0', 'B_2'])
self.assertArrayAlmostEqual(np.diag(infids),
uncorrelated_infids)
# Infidelity matrix should be hermitian
self.assertArrayEqual(infids, infids.conj().T)
count += 1
# Check raises
with self.assertRaises(TypeError):
# spectrum not callable
ff.infidelity(pulse, 2, omega, test_convergence=True)
with self.assertRaises(TypeError):
# omega not dict
ff.infidelity(pulse, lambda x: x, 2, test_convergence=True)
with self.assertRaises(ValueError):
# omega['spacing'] not in ('linear', 'log')
ff.infidelity(pulse,
lambda x: x, {'spacing': 2},
test_convergence=True)
with self.assertRaises(ValueError):
# which not total or correlation
ff.infidelity(pulse, spectra[0](S0, omega), omega, which=2)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse, spectra[0](S0, omega)[:10], omega)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse,
spectra[3](S0, omega),
omega,
n_oper_identifiers=['B_0', 'B_1', 'B_2'])
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse,
spectra[4](S0, omega)[:, [0]],
omega,
n_oper_identifiers=['B_0'])
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse, rng.standard_normal((2, 3, 4, len(omega))),
omega)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(testutil.rand_pulse_sequence(2, 3, n_nops=2),
rng.standard_normal((3, 2, 2, len(omega))), omega)
with self.assertRaises(ValueError):
# S cross-correlated but not hermitian
ff.infidelity(testutil.rand_pulse_sequence(2, 3, n_nops=2),
rng.standard_normal((2, 2, len(omega))), omega)
with self.assertRaises(ValueError):
# S 'cross-correlated' but not hermitian
ff.infidelity(pulse, (1 + 1j) * rng.standard_normal(
(1, 1, len(omega))), omega)
with self.assertRaises(NotImplementedError):
# smallness parameter for correlated noise source
ff.infidelity(pulse,
spectra[4](S0, omega),
omega,
n_oper_identifiers=['B_0', 'B_2'],
return_smallness=True)
def test_extend_with_identity(self):
"""Test extending a pulse to more qubits"""
ID, X, Y, Z = util.paulis
n_dt = 10
coeffs = rng.standard_normal((3, n_dt))
ids = ['X', 'Y', 'Z']
pulse = ff.PulseSequence(
list(zip((X, Y, Z), coeffs, ids)),
list(zip((X, Y, Z), np.ones((3, n_dt)), ids)),
np.ones(n_dt), basis=ff.Basis.pauli(1)
)
omega = util.get_sample_frequencies(pulse, spacing='log', n_samples=50)
for N in rng.randint(2, 5, 4):
for target in rng.randint(0, N-1, 2):
pulse.cleanup('all')
ext_opers = util.tensor(*np.insert(np.tile(ID, (N-1, 3, 1, 1)),
target, (X, Y, Z), axis=0))
# By default, extend should add the target qubit as suffix to
# identifiers
ext_ids = [i + f'_{target}' for i in ids]
ext_pulse = ff.PulseSequence(
list(zip(ext_opers, coeffs, ext_ids)),
list(zip(ext_opers, np.ones((3, n_dt)), ext_ids)),
np.ones(n_dt), basis=ff.Basis.pauli(N)
)
# Use custom mapping for identifiers and or labels
letters = rng.choice(list(string.ascii_letters), size=(3, 5))
mapped_ids = np.array([''.join(l) for l in letters])
mapping = {i: new_id for i, new_id in zip(ids, mapped_ids)}
ext_pulse_mapped_identifiers = ff.PulseSequence(
list(zip(ext_opers, coeffs, mapped_ids, ext_ids)),
list(zip(ext_opers, np.ones((3, n_dt)), mapped_ids,
ext_ids)),
np.ones(n_dt), basis=ff.Basis.pauli(N)
)
ext_pulse_mapped_labels = ff.PulseSequence(
list(zip(ext_opers, coeffs, ext_ids, mapped_ids)),
list(zip(ext_opers, np.ones((3, n_dt)), ext_ids,
mapped_ids)),
np.ones(n_dt), basis=ff.Basis.pauli(N)
)
ext_pulse_mapped_identifiers_labels = ff.PulseSequence(
list(zip(ext_opers, coeffs, mapped_ids)),
list(zip(ext_opers, np.ones((3, n_dt)), mapped_ids)),
np.ones(n_dt), basis=ff.Basis.pauli(N)
)
calc_filter_functionF = rng.randint(0, 2)
if calc_filter_functionF:
# Expect things to be cached in extended pulse if original
# also was cached
pulse.cache_filter_function(omega)
ext_pulse.cache_filter_function(omega)
test_ext_pulse = ff.extend([(pulse, target)], N, d_per_qubit=2)
test_ext_pulse_mapped_identifiers = ff.extend(
[(pulse, target, mapping)], N, d_per_qubit=2
)
test_ext_pulse_mapped_labels = ff.extend(
[(pulse, target, None, mapping)], N, d_per_qubit=2
)
test_ext_pulse_mapped_identifiers_labels = ff.extend(
[(pulse, target, mapping, mapping)], N, d_per_qubit=2
)
self.assertEqual(ext_pulse, test_ext_pulse)
self.assertEqual(ext_pulse_mapped_identifiers,
test_ext_pulse_mapped_identifiers)
self.assertEqual(ext_pulse_mapped_labels,
test_ext_pulse_mapped_labels)
self.assertEqual(ext_pulse_mapped_identifiers_labels,
test_ext_pulse_mapped_identifiers_labels)
if calc_filter_functionF:
self.assertCorrectDiagonalization(test_ext_pulse,
atol=1e-14)
self.assertArrayAlmostEqual(test_ext_pulse._propagators,
ext_pulse._propagators, atol=1e-14)
self.assertArrayAlmostEqual(
test_ext_pulse._total_propagator_liouville,
ext_pulse._total_propagator_liouville,
atol=1e-14
)
self.assertArrayAlmostEqual(
test_ext_pulse._total_propagator,
ext_pulse._total_propagator,
atol=1e-14
)
self.assertArrayAlmostEqual(test_ext_pulse._total_phases,
ext_pulse._total_phases)
self.assertArrayAlmostEqual(test_ext_pulse._control_matrix,
ext_pulse._control_matrix, atol=1e-12)
self.assertArrayAlmostEqual(
test_ext_pulse._filter_function,
ext_pulse._filter_function,
atol=1e-12
)
else:
self.assertIsNone(test_ext_pulse._eigvals)
self.assertIsNone(test_ext_pulse._eigvecs)
self.assertIsNone(test_ext_pulse._propagators)
self.assertIsNone(
test_ext_pulse._total_propagator_liouville)
self.assertIsNone(test_ext_pulse._total_propagator)
self.assertIsNone(test_ext_pulse._total_phases)
self.assertIsNone(test_ext_pulse._control_matrix)
self.assertIsNone(test_ext_pulse._filter_function)
pulse.cleanup('all')
ext_pulse.cleanup('all')
def test_tensor_insert(self):
I, X, Y, Z = util.paulis
arr = util.tensor(X, I)
with self.assertRaises(ValueError):
# Test exception for empty args
util.tensor_insert(arr, np.array([[]]), pos=0, arr_dims=[])
r = util.tensor_insert(arr, Y, Z, arr_dims=[[2, 2], [2, 2]], pos=0)
self.assertArrayAlmostEqual(r, util.tensor(Y, Z, X, I))
r = util.tensor_insert(arr, Y, Z, arr_dims=[[2, 2], [2, 2]], pos=1)
self.assertArrayAlmostEqual(r, util.tensor(X, Y, Z, I))
r = util.tensor_insert(arr, Y, Z, arr_dims=[[2, 2], [2, 2]], pos=2)
self.assertArrayAlmostEqual(r, util.tensor(X, I, Y, Z))
# Test pos being negative
r = util.tensor_insert(arr, Y, Z, arr_dims=[[2, 2], [2, 2]], pos=-1)
self.assertArrayAlmostEqual(r, util.tensor(X, Y, Z, I))
# Test pos exception
with self.assertRaises(IndexError) as err:
util.tensor_insert(arr, Y, Z, arr_dims=[[2, 2], [2, 2]], pos=3)
msg = 'Invalid position 3 specified. Must be between -2 and 2.'
self.assertEqual(msg, str(err.exception))
# Test broadcasting and rank != 2
A, B, C = (rng.standard_normal(
(2, 3, 1, 2)), rng.standard_normal(
(2, 3, 1, 2)), rng.standard_normal((3, 1, 3)))
arr = util.tensor(A, C, rank=1)
r = util.tensor_insert(arr, B, pos=1, rank=1, arr_dims=[[2, 3]])
self.assertArrayAlmostEqual(r, util.tensor(A, B, C, rank=1))
# Test exceptions for wrong arr_dims format
with self.assertRaises(ValueError):
util.tensor_insert(arr,
B,
pos=1,
rank=1,
arr_dims=[[3, 3], [1, 2], [2, 1]])
with self.assertRaises(ValueError):
util.tensor_insert(arr, B, pos=1, rank=1, arr_dims=[[2], [2, 1]])
A, B, C = (rng.standard_normal(
(2, 3, 1, 2)), rng.standard_normal(
(2, 3, 2, 2)), rng.standard_normal((3, 2, 1)))
arr = util.tensor(A, C, rank=3)
r = util.tensor_insert(arr,
B,
pos=1,
rank=3,
arr_dims=[[3, 3], [1, 2], [2, 1]])
self.assertArrayAlmostEqual(r, util.tensor(A, B, C, rank=3))
# Test exceptions for wrong arr_dims format
with self.assertRaises(ValueError):
util.tensor_insert(arr,
B,
pos=1,
rank=3,
arr_dims=[[1, 2], [2, 1]])
with self.assertRaises(ValueError):
util.tensor_insert(arr,
B,
pos=1,
rank=2,
arr_dims=[[3, 3, 1], [1, 2], [2]])
A, B, C = (rng.standard_normal((2, 1)), rng.standard_normal(
(1, 2, 3)), rng.standard_normal(1))
arr = util.tensor(A, C, rank=1)
r = util.tensor_insert(arr, B, pos=0, rank=1, arr_dims=[[1, 1]])
self.assertArrayAlmostEqual(r, util.tensor(A, B, C, rank=1))
arrs, args = [
rng.standard_normal((2, 2, 2)),
rng.standard_normal((2, 2, 2))
]
arr_dims = [[2, 2], [2, 2]]
r = util.tensor_insert(util.tensor(*arrs),
*args,
pos=(0, 1),
arr_dims=arr_dims)
self.assertArrayAlmostEqual(
r, util.tensor(args[0], arrs[0], args[1], arrs[1]))
r = util.tensor_insert(util.tensor(*arrs),
*args,
pos=(0, 0),
arr_dims=arr_dims)
self.assertArrayAlmostEqual(r, util.tensor(*args, *arrs))
r = util.tensor_insert(util.tensor(*arrs),
*args,
pos=(1, 2),
arr_dims=arr_dims)
self.assertArrayAlmostEqual(
r, util.tensor(*np.insert(arrs, (1, 2), args, axis=0)))
# Test exception for wrong pos argument
with self.assertRaises(ValueError):
util.tensor_insert(util.tensor(*arrs),
*args,
pos=(0, 1, 2),
arr_dims=arr_dims)
# Test exception for wrong shapes
arrs, args = (rng.standard_normal(
(2, 4, 3, 2)), rng.standard_normal((2, 2, 3, 4)))
with self.assertRaises(ValueError) as err:
util.tensor_insert(util.tensor(*arrs),
*args,
pos=(1, 2),
arr_dims=[[3, 3], [2, 2]])
err_msg = ('Could not insert arg 0 with shape (4, 9, 4) into the ' +
'array with shape (2, 3, 4) at position 1.')
cause_msg = ('Incompatible shapes (2, 3, 4) and (4, 9, 4) for ' +
'tensor product of rank 2.')
self.assertEqual(err_msg, str(err.exception))
self.assertEqual(cause_msg, str(err.exception.__cause__))
# Do some random tests
for rank, n_args, n_broadcast in zip(rng.randint(1, 4, 10),
rng.randint(3, 6, 10),
rng.randint(1, 11, 10)):
arrs = rng.randn(n_args, n_broadcast, *[2] * rank)
split_idx = rng.randint(1, n_args - 1)
ins_idx = rng.randint(split_idx - n_args, n_args - split_idx)
ins_arrs = arrs[:split_idx]
arr = util.tensor(*arrs[split_idx:], rank=rank)
sorted_arrs = np.insert(arrs[split_idx:],
ins_idx,
ins_arrs,
axis=0)
arr_dims = [[2] * (n_args - split_idx)] * rank
r = util.tensor_insert(arr,
*ins_arrs,
pos=ins_idx,
rank=rank,
arr_dims=arr_dims)
self.assertArrayAlmostEqual(r, util.tensor(*sorted_arrs,
rank=rank))
pos = rng.randint(-split_idx + 1, split_idx, split_idx)
r = util.tensor_insert(arr,
*ins_arrs,
pos=pos,
rank=rank,
arr_dims=arr_dims)
sorted_arrs = np.insert(arrs[split_idx:], pos, ins_arrs, axis=0)
self.assertArrayAlmostEqual(r,
util.tensor(*sorted_arrs, rank=rank),
atol=1e-10)
def test_accuracy(self):
ID, X, Y, Z = util.paulis
XI = util.tensor(X, ID)
IX = util.tensor(ID, X)
XII = util.tensor(X, ID, ID)
IXI = util.tensor(ID, X, ID)
IIX = util.tensor(ID, ID, X)
XIII = util.tensor(X, ID, ID, ID)
IXII = util.tensor(ID, X, ID, ID)
IIXI = util.tensor(ID, ID, X, ID)
IIIX = util.tensor(ID, ID, ID, X)
YI = util.tensor(Y, ID)
IY = util.tensor(ID, Y)
YII = util.tensor(Y, ID, ID)
IYI = util.tensor(ID, Y, ID)
IIY = util.tensor(ID, ID, Y)
YIII = util.tensor(Y, ID, ID, ID)
IYII = util.tensor(ID, Y, ID, ID)
IIYI = util.tensor(ID, ID, Y, ID)
IIIY = util.tensor(ID, ID, ID, Y)
ZI = util.tensor(Z, ID)
IZ = util.tensor(ID, Z)
ZII = util.tensor(Z, ID, ID)
IZI = util.tensor(ID, Z, ID)
ZIII = util.tensor(Z, ID, ID, ID)
IZII = util.tensor(ID, Z, ID, ID)
IIZI = util.tensor(ID, ID, Z, ID)
IIIZ = util.tensor(ID, ID, ID, Z)
IIZ = util.tensor(ID, ID, Z)
XXX = util.tensor(X, X, X)
n_dt = 10
coeffs = rng.standard_normal((3, n_dt))
X_pulse = ff.PulseSequence(
[[X, coeffs[0], 'X']],
list(zip((X, Y, Z), np.ones((3, n_dt)), ('X', 'Y', 'Z'))),
np.ones(n_dt),
basis=ff.Basis.pauli(1)
)
Y_pulse = ff.PulseSequence(
[[Y, coeffs[1], 'Y']],
list(zip((X, Y, Z), np.ones((3, n_dt)), ('X', 'Y', 'Z'))),
np.ones(n_dt),
basis=ff.Basis.pauli(1)
)
Z_pulse = ff.PulseSequence(
[[Z, coeffs[2], 'Z']],
list(zip((X, Y, Z), np.ones((3, n_dt)), ('X', 'Y', 'Z'))),
np.ones(n_dt),
basis=ff.Basis.pauli(1)
)
XZ_pulse = ff.PulseSequence(
[[XI, coeffs[0], 'XI'],
[IZ, coeffs[2], 'IZ']],
list(zip((XI, YI, ZI, IX, IY, IZ), np.ones((6, n_dt)),
('XI', 'YI', 'ZI', 'IX', 'IY', 'IZ'))),
np.ones(n_dt),
basis=ff.Basis.pauli(2)
)
XYZ_pulse = ff.PulseSequence(
[[XII, coeffs[0], 'XII'],
[IYI, coeffs[1], 'IYI'],
[IIZ, coeffs[2], 'IIZ']],
list(zip((XII, YII, ZII, IIX, IIY, IIZ, IXI, IYI, IZI, XXX),
np.ones((10, n_dt)),
('XII', 'YII', 'ZII', 'IIX', 'IIY', 'IIZ', 'IXI', 'IYI',
'IZI', 'XXX'))),
np.ones(n_dt),
basis=ff.Basis.pauli(3)
)
ZYX_pulse = ff.PulseSequence(
[[IIX, coeffs[0], 'IIX'],
[IYI, coeffs[1], 'IYI'],
[ZII, coeffs[2], 'ZII']],
list(zip((IIX, IIY, IIZ, XII, YII, ZII, IXI, IYI, IZI),
np.ones((9, n_dt)),
('IIX', 'IIY', 'IIZ', 'XII', 'YII', 'ZII', 'IXI', 'IYI',
'IZI'))),
np.ones(n_dt),
basis=ff.Basis.pauli(3)
)
XZXZ_pulse = ff.PulseSequence(
[[XIII, coeffs[0], 'XIII'],
[IZII, coeffs[2], 'IZII'],
[IIXI, coeffs[0], 'IIXI'],
[IIIZ, coeffs[2], 'IIIZ']],
list(zip((XIII, YIII, ZIII, IXII, IYII, IZII,
IIXI, IIYI, IIZI, IIIX, IIIY, IIIZ),
np.ones((12, n_dt)),
('XIII', 'YIII', 'ZIII', 'IXII', 'IYII', 'IZII',
'IIXI', 'IIYI', 'IIZI', 'IIIX', 'IIIY', 'IIIZ'))),
np.ones(n_dt),
basis=ff.Basis.pauli(4)
)
XXZZ_pulse = ff.PulseSequence(
[[XIII, coeffs[0], 'XIII'],
[IIZI, coeffs[2], 'IIZI'],
[IXII, coeffs[0], 'IXII'],
[IIIZ, coeffs[2], 'IIIZ']],
list(zip((XIII, YIII, ZIII, IIXI, IIYI, IIZI,
IXII, IYII, IZII, IIIX, IIIY, IIIZ),
np.ones((12, n_dt)),
('XIII', 'YIII', 'ZIII', 'IIXI', 'IIYI', 'IIZI',
'IXII', 'IYII', 'IZII', 'IIIX', 'IIIY', 'IIIZ'))),
np.ones(n_dt),
basis=ff.Basis.pauli(4)
)
# Cache
omega = ff.util.get_sample_frequencies(XYZ_pulse, n_samples=50)
X_pulse.cache_filter_function(omega)
Y_pulse.cache_filter_function(omega)
Z_pulse.cache_filter_function(omega)
XZ_pulse.cache_filter_function(omega)
XYZ_pulse.cache_filter_function(omega)
ZYX_pulse.cache_filter_function(omega)
XZXZ_pulse.cache_filter_function(omega)
XXZZ_pulse.cache_filter_function(omega)
# Test that mapping a pulse to itself returns the pulse itself and
# issues a warning
with self.assertWarns(UserWarning):
pulse = ff.extend([(X_pulse, 0)])
self.assertIs(pulse, X_pulse)
with self.assertWarns(UserWarning):
pulse = ff.extend([(XZ_pulse, (0, 1))])
self.assertIs(pulse, XZ_pulse)
# Test mapping two single-qubit pulses to a two-qubit pulse
XZ_pulse_ext = ff.extend([
(X_pulse, 0, {'X': 'XI', 'Y': 'YI', 'Z': 'ZI'}),
(Z_pulse, 1, {'X': 'IX', 'Y': 'IY', 'Z': 'IZ'})
])
self.assertEqual(XZ_pulse, XZ_pulse_ext)
self.assertCorrectDiagonalization(XZ_pulse_ext, atol=1e-14)
self.assertArrayAlmostEqual(XZ_pulse._propagators,
XZ_pulse_ext._propagators, atol=1e-10)
self.assertArrayAlmostEqual(XZ_pulse._control_matrix,
XZ_pulse_ext._control_matrix, atol=1e-9)
self.assertArrayAlmostEqual(XZ_pulse._filter_function,
XZ_pulse_ext._filter_function, atol=1e-9)
# Test additional noise Hamiltonian
add_H_n = list(zip((XXX,), np.ones((1, n_dt)), ['XXX']))
XYZ_pulse_ext = ff.extend(
[(XZ_pulse, (0, 2),
{i: i[0] + 'I' + i[1] for i in XZ_pulse.n_oper_identifiers}),
(Y_pulse, 1,
{i: 'I' + i[0] + 'I' for i in Y_pulse.n_oper_identifiers})],
additional_noise_Hamiltonian=add_H_n
)
self.assertEqual(XYZ_pulse, XYZ_pulse_ext)
self.assertCorrectDiagonalization(XYZ_pulse_ext, atol=1e-14)
self.assertArrayAlmostEqual(XYZ_pulse._propagators,
XYZ_pulse_ext._propagators, atol=1e-10)
self.assertArrayAlmostEqual(XYZ_pulse._control_matrix,
XYZ_pulse_ext._control_matrix, atol=1e-9)
self.assertArrayAlmostEqual(XYZ_pulse._filter_function,
XYZ_pulse_ext._filter_function, atol=1e-9)
# Test remapping a two-qubit pulse
ZYX_pulse_ext = ff.extend(
[(XZ_pulse, (2, 0),
{i: i[1] + 'I' + i[0] for i in XZ_pulse.n_oper_identifiers}),
(Y_pulse, 1,
{i: 'I' + i[0] + 'I' for i in Y_pulse.n_oper_identifiers})],
)
self.assertEqual(ZYX_pulse, ZYX_pulse_ext)
self.assertCorrectDiagonalization(ZYX_pulse_ext, atol=1e-14)
self.assertArrayAlmostEqual(ZYX_pulse._propagators,
ZYX_pulse_ext._propagators, atol=1e-10)
self.assertArrayAlmostEqual(ZYX_pulse._control_matrix,
ZYX_pulse_ext._control_matrix, atol=1e-9)
self.assertArrayAlmostEqual(ZYX_pulse._filter_function,
ZYX_pulse_ext._filter_function, atol=1e-9)
XZXZ_pulse_ext = ff.extend([
(XZ_pulse, (0, 1),
{i: i + 'II' for i in XZ_pulse.n_oper_identifiers}),
(XZ_pulse, (2, 3),
{i: 'II' + i for i in XZ_pulse.n_oper_identifiers})
], cache_diagonalization=True)
self.assertEqual(XZXZ_pulse, XZXZ_pulse_ext)
self.assertCorrectDiagonalization(XZXZ_pulse_ext, atol=1e-14)
self.assertArrayAlmostEqual(XZXZ_pulse._propagators,
XZXZ_pulse_ext._propagators, atol=1e-10)
self.assertArrayAlmostEqual(XZXZ_pulse._control_matrix,
XZXZ_pulse_ext._control_matrix, atol=1e-9)
self.assertArrayAlmostEqual(XZXZ_pulse._filter_function,
XZXZ_pulse_ext._filter_function, atol=1e-8)
XZXZ_pulse_ext = ff.extend([
(XZ_pulse, (0, 1),
{i: i + 'II' for i in XZ_pulse.n_oper_identifiers}),
(XZ_pulse, (2, 3),
{i: 'II' + i for i in XZ_pulse.n_oper_identifiers})
], cache_diagonalization=False)
self.assertEqual(XZXZ_pulse, XZXZ_pulse_ext)
self.assertArrayAlmostEqual(XZXZ_pulse._total_propagator,
XZXZ_pulse_ext._total_propagator,
atol=1e-10)
# Test merging with overlapping qubit ranges
XXZZ_pulse_ext = ff.extend([
(XZ_pulse, (0, 2),
{i: i[0] + 'I' + i[1] + 'I'
for i in XZ_pulse.n_oper_identifiers}),
(XZ_pulse, (1, 3),
{i: 'I' + i[0] + 'I' + i[1]
for i in XZ_pulse.n_oper_identifiers}),
])
self.assertEqual(XXZZ_pulse, XXZZ_pulse_ext)
self.assertCorrectDiagonalization(XXZZ_pulse_ext, atol=1e-14)
self.assertArrayAlmostEqual(XXZZ_pulse._propagators,
XXZZ_pulse_ext._propagators, atol=1e-10)
self.assertArrayAlmostEqual(XXZZ_pulse._control_matrix,
XXZZ_pulse_ext._control_matrix, atol=1e-10)
self.assertArrayAlmostEqual(XXZZ_pulse._filter_function,
XXZZ_pulse_ext._filter_function, atol=1e-8)
def test_abs2(self):
x = rng.standard_normal((20, 100)) + 1j * rng.standard_normal(
(20, 100))
self.assertArrayAlmostEqual(np.abs(x)**2, util.abs2(x))
def test_tensor_merge(self):
# Test basic functionality
I, X, Y, Z = util.paulis
arr = util.tensor(X, Y, Z)
ins = util.tensor(I, I)
r1 = util.tensor_merge(arr,
ins,
pos=[1, 2],
arr_dims=[[2] * 3, [2] * 3],
ins_dims=[[2] * 2, [2] * 2])
r2 = util.tensor_merge(ins,
arr,
pos=[0, 1, 2],
arr_dims=[[2] * 2, [2] * 2],
ins_dims=[[2] * 3, [2] * 3])
self.assertArrayAlmostEqual(r1, util.tensor(X, I, Y, I, Z))
self.assertArrayAlmostEqual(r1, r2)
# Test if tensor_merge and tensor_insert produce same results
arr = util.tensor(Y, Z)
ins = util.tensor(I, X)
r1 = util.tensor_merge(arr,
ins,
pos=[0, 0],
arr_dims=[[2] * 2, [2] * 2],
ins_dims=[[2] * 2, [2] * 2])
r2 = util.tensor_insert(arr,
I,
X,
pos=[0, 0],
arr_dims=[[2] * 2, [2] * 2])
self.assertArrayAlmostEqual(r1, r2)
# Test pos being negative
r = util.tensor_merge(arr,
ins,
arr_dims=[[2, 2], [2, 2]],
ins_dims=[[2, 2], [2, 2]],
pos=(-1, -2))
self.assertArrayAlmostEqual(r, util.tensor(X, Y, I, Z))
# Test exceptions
# Wrong dims format
with self.assertRaises(ValueError):
util.tensor_merge(arr,
ins,
pos=(1, 2),
arr_dims=[[2, 2], [2, 2], [2, 2]],
ins_dims=[[2, 2], [2, 2]])
with self.assertRaises(ValueError):
util.tensor_merge(arr,
ins,
pos=(1, 2),
arr_dims=[[2, 2], [2, 2]],
ins_dims=[[2, 2], [2, 2], [2, 2]])
with self.assertRaises(ValueError):
util.tensor_merge(arr,
ins,
pos=(1, 2),
arr_dims=[[2, 2], [2, 2, 2]],
ins_dims=[[2, 2], [2, 2]])
with self.assertRaises(ValueError):
util.tensor_merge(arr,
ins,
pos=(1, 2),
arr_dims=[[2, 2], [2, 2]],
ins_dims=[[2, 2], [2, 2, 2]])
# Wrong pos
with self.assertRaises(IndexError):
util.tensor_merge(arr,
ins,
pos=(1, 3),
arr_dims=[[2, 2], [2, 2]],
ins_dims=[[2, 2], [2, 2]])
# Wrong dimensions given
with self.assertRaises(ValueError):
util.tensor_merge(arr,
ins,
pos=(1, 2),
arr_dims=[[2, 3], [2, 2]],
ins_dims=[[2, 2], [2, 2]])
with self.assertRaises(ValueError):
util.tensor_merge(arr,
ins,
pos=(1, 2),
arr_dims=[[2, 2], [2, 2]],
ins_dims=[[2, 3], [2, 2]])
# Incompatible shapes
arrs, args = (rng.standard_normal(
(2, 4, 3, 2)), rng.standard_normal((2, 2, 3, 4)))
with self.assertRaises(ValueError) as err:
util.tensor_merge(util.tensor(*arrs),
util.tensor(*args),
pos=(1, 2),
arr_dims=[[3, 3], [2, 2]],
ins_dims=[[3, 3], [4, 4]])
msg = ('Incompatible shapes (2, 9, 16) and (4, 9, 4) for tensor ' +
'product of rank 2.')
self.assertEqual(msg, str(err.exception))
# Test rank 1 and broadcasting
arr = rng.standard_normal((2, 10, 3, 4))
ins = rng.standard_normal((2, 10, 3, 2))
r = util.tensor_merge(util.tensor(*arr, rank=1),
util.tensor(*ins, rank=1),
pos=[0, 1],
arr_dims=[[4, 4]],
ins_dims=[[2, 2]],
rank=1)
self.assertArrayAlmostEqual(
r, util.tensor(ins[0], arr[0], ins[1], arr[1], rank=1))
# Do some random tests
for rank, n_args, n_broadcast in zip(rng.randint(1, 4, 10),
rng.randint(3, 6, 10),
rng.randint(1, 11, 10)):
arrs = rng.standard_normal((n_args, n_broadcast, *[2] * rank))
split_idx = rng.randint(1, n_args - 1)
arr = util.tensor(*arrs[split_idx:], rank=rank)
ins = util.tensor(*arrs[:split_idx], rank=rank)
pos = rng.randint(0, split_idx, split_idx)
sorted_arrs = np.insert(arrs[split_idx:],
pos,
arrs[:split_idx],
axis=0)
arr_dims = [[2] * (n_args - split_idx)] * rank
ins_dims = [[2] * split_idx] * rank
r = util.tensor_merge(arr,
ins,
pos=pos,
rank=rank,
arr_dims=arr_dims,
ins_dims=ins_dims)
self.assertArrayAlmostEqual(r, util.tensor(*sorted_arrs,
rank=rank))
def test_pulse_sequence_attributes(self):
"""Test attributes of single instance"""
X, Y, Z = util.paulis[1:]
n_dt = rng.randint(1, 10)
# trivial case
A = ff.PulseSequence([[X, rng.standard_normal(n_dt), 'X']],
[[Z, rng.standard_normal(n_dt), 'Z']],
np.abs(rng.standard_normal(n_dt)))
self.assertFalse(A == 1)
self.assertTrue(A != 1)
# different number of time steps
B = ff.PulseSequence([[X, rng.standard_normal(n_dt+1), 'X']],
[[Z, rng.standard_normal(n_dt+1), 'Z']],
np.abs(rng.standard_normal(n_dt+1)))
self.assertFalse(A == B)
self.assertTrue(A != B)
# different time steps
B = ff.PulseSequence(
list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
np.abs(rng.standard_normal(n_dt))
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different control opers
B = ff.PulseSequence(
list(zip([Y], A.c_coeffs, A.c_oper_identifiers)),
list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
A.dt
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different control coeffs
B = ff.PulseSequence(
list(zip(A.c_opers, [rng.standard_normal(n_dt)],
A.c_oper_identifiers)),
list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
A.dt
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different noise opers
B = ff.PulseSequence(
list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
list(zip([Y], A.n_coeffs, A.n_oper_identifiers)),
A.dt
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different noise coeffs
B = ff.PulseSequence(
list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
list(zip(A.n_opers, [rng.standard_normal(n_dt)],
A.n_oper_identifiers)),
A.dt
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different control oper identifiers
B = ff.PulseSequence(
list(zip(A.c_opers, A.c_coeffs, ['foobar'])),
list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
A.dt
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different noise oper identifiers
B = ff.PulseSequence(
list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
list(zip(A.n_opers, A.n_coeffs, ['foobar'])),
A.dt
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# different bases
elem = testutil.rand_herm_traceless(2)
B = ff.PulseSequence(
list(zip(A.c_opers, A.c_coeffs, A.c_oper_identifiers)),
list(zip(A.n_opers, A.n_coeffs, A.n_oper_identifiers)),
A.dt,
ff.Basis(elem)
)
self.assertFalse(A == B)
self.assertTrue(A != B)
# Test sparse operators for whatever reason
A = ff.PulseSequence([[util.paulis[1], [1]]],
[[sparse.COO.from_numpy(util.paulis[2]), [2]]],
[3])
B = ff.PulseSequence([[sparse.COO.from_numpy(util.paulis[1]), [1]]],
[[util.paulis[2], [2]]],
[3])
self.assertEqual(A, B)
# Test for attributes
for attr in A.__dict__.keys():
if not (attr.startswith('_') or '_' + attr in A.__dict__.keys()):
# not a cached attribute
with self.assertRaises(AttributeError):
_ = A.is_cached(attr)
else:
# set mock attribute at random
if rng.randint(0, 2):
setattr(A, attr, 'foo')
assertion = self.assertTrue
else:
setattr(A, attr, None)
assertion = self.assertFalse
assertion(A.is_cached(attr))
# Diagonalization attributes
A.diagonalize()
self.assertIsNotNone(A.eigvals)
self.assertIsNotNone(A.eigvecs)
self.assertIsNotNone(A.propagators)
A.cleanup('conservative')
self.assertIsNotNone(A.eigvals)
A.cleanup('conservative')
self.assertIsNotNone(A.eigvecs)
A.cleanup('conservative')
self.assertIsNotNone(A.propagators)
aliases = {'eigenvalues': '_eigvals',
'eigenvectors': '_eigvecs',
'total propagator': '_total_propagator',
'total propagator liouville': '_total_propagator_liouville',
'frequencies': '_omega',
'total phases': '_total_phases',
'filter function': '_filter_function',
'fidelity filter function': '_filter_function',
'generalized filter function': '_filter_function_gen',
'pulse correlation filter function': '_filter_function_pc',
'fidelity pulse correlation filter function': '_filter_function_pc',
'generalized pulse correlation filter function': '_filter_function_pc_gen',
'control matrix': '_control_matrix',
'pulse correlation control matrix': '_control_matrix_pc'}
for alias, attr in aliases.items():
# set mock attribute at random
if rng.randint(0, 2):
setattr(A, attr, 'foo')
assertion = self.assertTrue
else:
setattr(A, attr, None)
assertion = self.assertFalse
assertion(A.is_cached(alias))
assertion(A.is_cached(alias.upper()))
assertion(A.is_cached(alias.replace(' ', '_')))
A.cleanup('all')
# Test cleanup
C = ff.concatenate((A, A), calc_pulse_correlation_FF=True,
which='generalized',
omega=util.get_sample_frequencies(A))
C.diagonalize()
attrs = ['_eigvals', '_eigvecs', '_propagators']
for attr in attrs:
self.assertIsNotNone(getattr(C, attr))
C.cleanup()
for attr in attrs:
self.assertIsNone(getattr(C, attr))
C.diagonalize()
C.cache_control_matrix(A.omega)
attrs.extend(['_control_matrix', '_total_phases', '_total_propagator',
'_total_propagator_liouville'])
for attr in attrs:
self.assertIsNotNone(getattr(C, attr))
C.cleanup('greedy')
for attr in attrs:
self.assertIsNone(getattr(C, attr))
C.cache_filter_function(A.omega, which='generalized')
for attr in attrs + ['omega', '_filter_function_gen',
'_filter_function_pc_gen']:
self.assertIsNotNone(getattr(C, attr))
C = ff.concatenate((A, A), calc_pulse_correlation_FF=True,
which='fidelity', omega=A.omega)
C.diagonalize()
C.cache_filter_function(A.omega, which='fidelity')
attrs.extend(['omega', '_filter_function', '_filter_function_pc'])
for attr in attrs:
self.assertIsNotNone(getattr(C, attr))
C.cleanup('all')
for attr in attrs + ['_filter_function_gen',
'_filter_function_pc_gen']:
self.assertIsNone(getattr(C, attr))
C.cache_filter_function(A.omega, which='fidelity')
C.cleanup('frequency dependent')
freq_attrs = {'omega', '_control_matrix', '_filter_function',
'_filter_function_gen', '_filter_function_pc',
'_filter_function_pc_gen', '_total_phases'}
for attr in freq_attrs:
self.assertIsNone(getattr(C, attr))
for attr in set(attrs).difference(freq_attrs):
self.assertIsNotNone(getattr(C, attr))
def test_pulse_sequence_attributes_concat(self):
"""Test attributes of concatenated sequence."""
X, Y, Z = util.paulis[1:]
n_dt_1 = rng.randint(5, 11)
x_coeff_1 = rng.standard_normal(n_dt_1)
z_coeff_1 = rng.standard_normal(n_dt_1)
dt_1 = np.abs(rng.standard_normal(n_dt_1))
n_dt_2 = rng.randint(5, 11)
y_coeff_2 = rng.standard_normal(n_dt_2)
z_coeff_2 = rng.standard_normal(n_dt_2)
dt_2 = np.abs(rng.standard_normal(n_dt_2))
pulse_1 = ff.PulseSequence([[X, x_coeff_1]],
[[Z, z_coeff_1]],
dt_1)
pulse_2 = ff.PulseSequence([[Y, y_coeff_2]],
[[Z, z_coeff_2]],
dt_2)
pulse_3 = ff.PulseSequence([[Y, rng.standard_normal(2)],
[X, rng.standard_normal(2)]],
[[Z, np.abs(rng.standard_normal(2))]],
[1, 1])
# Concatenate with different noise opers
pulses = [testutil.rand_pulse_sequence(2, 1) for _ in range(2)]
pulses[0].omega = np.arange(10)
pulses[1].omega = np.arange(10)
newpulse = ff.concatenate(pulses, calc_filter_function=True)
self.assertTrue(newpulse.is_cached('filter function'))
pulse_12 = pulse_1 @ pulse_2
pulse_21 = pulse_2 @ pulse_1
with self.assertRaises(TypeError):
_ = pulse_1 @ rng.standard_normal((2, 2))
# Concatenate pulses with same operators but different labels
with self.assertRaises(ValueError):
pulse_1 @ pulse_3
# Test nbytes property
_ = pulse_1.nbytes
self.assertArrayEqual(pulse_12.dt, [*dt_1, *dt_2])
self.assertArrayEqual(pulse_21.dt, [*dt_2, *dt_1])
self.assertArrayEqual(pulse_12.c_opers, [X, Y])
self.assertArrayEqual(pulse_21.c_opers, [Y, X])
self.assertArrayEqual(pulse_12.c_oper_identifiers, ['A_0_0', 'A_0_1'])
self.assertArrayEqual(pulse_21.c_oper_identifiers, ['A_0_0', 'A_0_1'])
self.assertArrayEqual(pulse_12.c_coeffs,
[[*x_coeff_1, *np.zeros(n_dt_2)],
[*np.zeros(n_dt_1), *y_coeff_2]])
self.assertArrayEqual(pulse_21.c_coeffs,
[[*y_coeff_2, *np.zeros(n_dt_1)],
[*np.zeros(n_dt_2), *x_coeff_1]])
self.assertArrayEqual(pulse_12.n_opers, [Z])
self.assertArrayEqual(pulse_21.n_opers, [Z])
self.assertArrayEqual(pulse_12.n_oper_identifiers, ['B_0'])
self.assertArrayEqual(pulse_21.n_oper_identifiers, ['B_0'])
self.assertArrayEqual(pulse_12.n_coeffs, [[*z_coeff_1, *z_coeff_2]])
self.assertArrayEqual(pulse_21.n_coeffs, [[*z_coeff_2, *z_coeff_1]])
omega = np.linspace(-100, 100, 101)
pulses = (pulse_1, pulse_2, pulse_12, pulse_21)
for pulse in pulses:
self.assertIsNone(pulse._total_phases)
self.assertIsNone(pulse._total_propagator)
self.assertIsNone(pulse._total_propagator_liouville)
total_phases = pulse.get_total_phases(omega)
total_propagator = pulse.total_propagator
total_propagator_liouville = pulse.total_propagator_liouville
self.assertArrayEqual(total_phases, pulse._total_phases)
self.assertArrayEqual(total_propagator, pulse._total_propagator)
self.assertArrayEqual(total_propagator_liouville,
pulse._total_propagator_liouville)
# Test custom identifiers
letters = rng.choice(list(string.ascii_letters), size=(6, 5),
replace=False)
ids = [''.join(c) for c in letters[:3]]
labels = [''.join(c) for c in letters[3:]]
pulse = ff.PulseSequence(
list(zip([X, Y, Z], rng.standard_normal((3, 2)), ids, labels)),
list(zip([X, Y, Z], rng.standard_normal((3, 2)), ids, labels)),
[1, 1]
)
self.assertArrayEqual(pulse.c_oper_identifiers, sorted(ids))
self.assertArrayEqual(pulse.n_oper_identifiers, sorted(ids))
def test_infidelity(self):
"""Benchmark infidelity results against previous version's results"""
rng.seed(123456789)
spectra = [
lambda S0, omega: S0 * abs(omega)**0,
lambda S0, omega: S0 / abs(omega)**0.7,
lambda S0, omega: S0 * np.exp(-abs(omega)),
# different spectra for different n_opers
lambda S0, omega: np.array(
[S0 * abs(omega)**0, S0 / abs(omega)**0.7]),
# cross-correlated spectra
lambda S0, omega: np.array([[
S0 / abs(omega)**0.7, S0 / (1 + omega**2) + 1j * S0 * omega
], [S0 / (1 + omega**2) - 1j * S0 * omega, S0 / abs(omega)**0.7]])
]
ref_infids = ([[0.415494970094, 0.89587362496],
[0.493004378474, 0.812378971328],
[0.133466914361, 0.197411969384],
[0.415494970094, 0.812378971328],
[[0.493004378474, 0.435140425045],
[0.435140425045, 0.812378971328]],
[3.62995021962, 2.938710386281],
[2.302617869945, 2.6187737025],
[0.506821680978, 0.695495602872],
[3.62995021962, 2.6187737025],
[[2.302617869945, 0.58515469294],
[0.58515469294, 2.6187737025]],
[2.822636459567, 1.205901937127],
[1.63758822101, 1.236844976323],
[0.324175447082, 0.329789052239],
[2.822636459567, 1.236844976323],
[[1.63758822101, 0.72007826813],
[0.72007826813, 1.236844976323]]])
count = 0
for d in (2, 3, 4):
pulse = testutil.rand_pulse_sequence(d, 10, 2, 3)
pulse.n_oper_identifiers = np.array(['B_0', 'B_2'])
omega = np.geomspace(0.1, 10, 51)
S0 = np.abs(rng.standard_normal())
for spec in spectra:
S = spec(S0, omega)
infids = ff.infidelity(pulse,
S,
omega,
n_oper_identifiers=['B_0', 'B_2'])
self.assertArrayAlmostEqual(infids,
ref_infids[count],
atol=1e-12)
if S.ndim == 3:
# Diagonal of the infidelity matrix should correspond to
# uncorrelated terms
uncorrelated_infids = ff.infidelity(
pulse,
S[range(2), range(2)],
omega,
n_oper_identifiers=['B_0', 'B_2'])
self.assertArrayAlmostEqual(np.diag(infids),
uncorrelated_infids)
# Infidelity matrix should be hermitian
self.assertArrayEqual(infids, infids.conj().T)
count += 1
# Check raises
with self.assertRaises(TypeError):
# spectrum not callable
ff.infidelity(pulse, 2, omega, test_convergence=True)
with self.assertRaises(TypeError):
# omega not dict
ff.infidelity(pulse, lambda x: x, 2, test_convergence=True)
with self.assertRaises(ValueError):
# omega['spacing'] not in ('linear', 'log')
ff.infidelity(pulse,
lambda x: x, {'spacing': 2},
test_convergence=True)
with self.assertRaises(ValueError):
# which not total or correlation
ff.infidelity(pulse, spectra[0](S0, omega), omega, which=2)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse, spectra[0](S0, omega)[:10], omega)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse,
spectra[3](S0, omega),
omega,
n_oper_identifiers=['B_0', 'B_1', 'B_2'])
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse,
spectra[4](S0, omega)[:, [0]],
omega,
n_oper_identifiers=['B_0'])
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse, rng.standard_normal((2, 3, 4, len(omega))),
omega)
with self.assertRaises(NotImplementedError):
# smallness parameter for correlated noise source
ff.infidelity(pulse,
spectra[4](S0, omega),
omega,
n_oper_identifiers=['B_0', 'B_2'],
return_smallness=True)
def test_basis_constructor(self):
"""Test the constructor for several failure modes"""
# Constructing from given elements should check for __getitem__
with self.assertRaises(TypeError):
_ = ff.Basis(1)
# All elements should be either sparse, Qobj, or ndarray
elems = [
util.paulis[1],
COO.from_numpy(util.paulis[3]), [[0, 1], [1, 0]]
]
with self.assertRaises(TypeError):
_ = ff.Basis(elems)
# Too many elements
with self.assertRaises(ValueError):
_ = ff.Basis(rng.standard_normal((5, 2, 2)))
# Non traceless elems but traceless basis requested
with self.assertRaises(ValueError):
_ = ff.Basis.from_partial(np.ones((2, 2)), traceless=True)
# Incorrect number of labels for default constructor
with self.assertRaises(ValueError):
_ = ff.Basis(util.paulis, labels=['a', 'b', 'c'])
# Incorrect number of labels for from_partial constructor
with self.assertRaises(ValueError):
_ = ff.Basis.from_partial(util.paulis[:2], labels=['a', 'b', 'c'])
# from_partial constructor should move identity label to the front
basis = ff.Basis.from_partial([util.paulis[1], util.paulis[0]],
labels=['x', 'i'])
self.assertEqual(basis.labels[:2], ['i', 'x'])
self.assertEqual(basis.labels[2:], ['$C_{2}$', '$C_{3}$'])
# from_partial constructor should copy labels if it can
partial_basis = ff.Basis.pauli(1)[[1, 3]]
partial_basis.labels = [
partial_basis.labels[1], partial_basis.labels[3]
]
basis = ff.Basis.from_partial(partial_basis)
self.assertEqual(basis.labels[:2], partial_basis.labels)
self.assertEqual(basis.labels[2:], ['$C_{2}$', '$C_{3}$'])
# Default constructor should return 3d array also for single 2d element
basis = ff.Basis(rng.standard_normal((2, 2)))
self.assertEqual(basis.shape, (1, 2, 2))
# from_partial constructor should return same basis for 2d or 3d input
elems = testutil.rand_herm(3)
basis1 = ff.Basis.from_partial(elems, labels=['weif'])
basis2 = ff.Basis.from_partial(elems.squeeze(), labels=['weif'])
self.assertEqual(basis1, basis2)
# Calling with only the identity should work with traceless true or false
self.assertEqual(ff.Basis(np.eye(2), traceless=False),
ff.Basis(np.eye(2), traceless=True))
# Constructing a basis from a basis should work
_ = ff.Basis(ff.Basis.ggm(2)[1:])
# Constructing should not change the elements
elems = rng.standard_normal((6, 3, 3))
basis = ff.Basis(elems)
self.assertArrayEqual(elems, basis)
def test_basis_expansion_and_normalization(self):
"""Correct expansion of operators and normalization of bases"""
# dtype
b = ff.Basis.ggm(3)
r = ff.basis.expand(rng.standard_normal((3, 3)), b, hermitian=False)
self.assertTrue(r.dtype == 'complex128')
r = ff.basis.expand(testutil.rand_herm(3), b, hermitian=True)
self.assertTrue(r.dtype == 'float64')
b._isherm = False
r = ff.basis.expand(testutil.rand_herm(3), b, hermitian=True)
self.assertTrue(r.dtype == 'complex128')
r = ff.basis.ggm_expand(testutil.rand_herm(3), hermitian=True)
self.assertTrue(r.dtype == 'float64')
r = ff.basis.ggm_expand(rng.standard_normal((3, 3)), hermitian=False)
self.assertTrue(r.dtype == 'complex128')
for _ in range(10):
d = rng.integers(2, 16)
ggm_basis = ff.Basis.ggm(d)
basis = ff.Basis(
np.einsum('i,ijk->ijk', rng.standard_normal(d**2), ggm_basis))
M = rng.standard_normal((d, d)) + 1j * rng.standard_normal((d, d))
M -= np.trace(M) / d
coeffs = ff.basis.expand(M, basis, normalized=False)
self.assertArrayAlmostEqual(M, np.einsum('i,ijk', coeffs, basis))
self.assertArrayAlmostEqual(ff.basis.expand(M, ggm_basis),
ff.basis.ggm_expand(M),
atol=1e-14)
self.assertArrayAlmostEqual(ff.basis.ggm_expand(M),
ff.basis.ggm_expand(M, traceless=True),
atol=1e-14)
n = rng.integers(1, 50)
M = rng.standard_normal((n, d, d)) + 1j * rng.standard_normal(
(n, d, d))
coeffs = ff.basis.expand(M, basis, normalized=False)
self.assertArrayAlmostEqual(
M, np.einsum('li,ijk->ljk', coeffs, basis))
self.assertArrayAlmostEqual(ff.basis.expand(M, ggm_basis),
ff.basis.ggm_expand(M),
atol=1e-14)
# Argument to ggm_expand not square in last two dimensions
with self.assertRaises(ValueError):
ff.basis.ggm_expand(basis[..., 0])
self.assertTrue(ff.basis.normalize(basis).isorthonorm)
# Basis method and function should give the same
normalized = ff.basis.normalize(basis)
basis.normalize()
self.assertEqual(normalized, basis)
# normalize single element
elem = basis[1]
normalized = ff.basis.normalize(elem)
elem.normalize()
self.assertEqual(normalized, elem)
# Not matrix or sequence of matrices
with self.assertRaises(ValueError):
ff.basis.normalize(basis[0, 0])
# Test copy
arr = rng.standard_normal((3, 2, 2))
basis = ff.Basis(arr)
normalized = basis.normalize(copy=True)
self.assertIsNot(normalized, basis)
self.assertFalse(np.array_equal(normalized, basis))
basis.normalize()
self.assertArrayEqual(normalized, basis)
def test_mdot(self):
arr = rng.standard_normal((3, 2, 4, 4))
self.assertArrayEqual(util.mdot(arr, 0), arr[0] @ arr[1] @ arr[2])
self.assertArrayEqual(util.mdot(arr, 1), arr[:, 0] @ arr[:, 1])
def test_infidelity(self):
"""Benchmark infidelity results against previous version's results"""
rng.seed(123456789)
spectra = [
lambda S0, omega: S0 * omega**0,
lambda S0, omega: S0 / omega**0.7,
lambda S0, omega: S0 * np.exp(-omega),
# different spectra for different n_opers
lambda S0, omega: np.array([S0 * omega**0, S0 / omega**0.7]),
# cross-correlated spectra
lambda S0, omega: np.array([[
S0 / omega**0.7, (1 + 1j) * S0 * np.exp(-omega)
], [(1 - 1j) * S0 * np.exp(-omega), S0 / omega**0.7]])
]
ref_infids = ([0.448468950307,
0.941871479562], [0.65826575772, 1.042914346335],
[0.163303005479,
0.239032549377], [0.448468950307, 1.042914346335],
[[0.65826575772, 0.069510589685 + 0.069510589685j],
[0.069510589685 - 0.069510589685j,
1.042914346335]], [3.687399348243, 3.034914820757],
[2.590545568435,
3.10093804628], [0.55880380219, 0.782544974968
], [3.687399348243, 3.10093804628],
[[2.590545568435, -0.114514760108 - 0.114514760108j],
[-0.114514760108 + 0.114514760108j,
3.10093804628]], [2.864567451344, 1.270260393902], [
1.847740998731, 1.559401345443
], [0.362116177417,
0.388022992097], [2.864567451344, 1.559401345443],
[[1.847740998731, 0.088373663409 + 0.088373663409j],
[0.088373663409 - 0.088373663409j, 1.559401345443]])
count = 0
for d in (2, 3, 4):
pulse = testutil.rand_pulse_sequence(d, 10, 2, 3)
pulse.n_oper_identifiers = np.array(['B_0', 'B_2'])
omega = np.geomspace(0.1, 10, 51)
S0 = np.abs(rng.standard_normal())
for spec in spectra:
S, omega_t = ff.util.symmetrize_spectrum(
spec(S0, omega), omega)
infids = ff.infidelity(pulse,
S,
omega_t,
n_oper_identifiers=['B_0', 'B_2'])
self.assertArrayAlmostEqual(infids,
ref_infids[count],
atol=1e-12)
if S.ndim == 3:
# Diagonal of the infidelity matrix should correspond to
# uncorrelated terms
uncorrelated_infids = ff.infidelity(
pulse,
S[range(2), range(2)],
omega_t,
n_oper_identifiers=['B_0', 'B_2'])
self.assertArrayAlmostEqual(np.diag(infids),
uncorrelated_infids)
# Infidelity matrix should be hermitian
self.assertArrayEqual(infids, infids.conj().T)
count += 1
# Check raises
with self.assertRaises(TypeError):
# spectrum not callable
ff.infidelity(pulse, 2, omega_t, test_convergence=True)
with self.assertRaises(TypeError):
# omega not dict
ff.infidelity(pulse, lambda x: x, 2, test_convergence=True)
with self.assertRaises(ValueError):
# omega['spacing'] not in ('linear', 'log')
ff.infidelity(pulse,
lambda x: x, {'spacing': 2},
test_convergence=True)
with self.assertRaises(ValueError):
# which not total or correlation
ff.infidelity(pulse, spectra[0](S0, omega_t), omega, which=2)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse, spectra[0](S0, omega_t)[:10], omega)
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse,
spectra[3](S0, omega),
omega,
n_oper_identifiers=['B_0', 'B_1', 'B_2'])
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse,
spectra[4](S0, omega)[:, [0]],
omega,
n_oper_identifiers=['B_0'])
with self.assertRaises(ValueError):
# S wrong dimensions
ff.infidelity(pulse, rng.standard_normal((2, 3, 4, len(omega))),
omega)
with self.assertRaises(NotImplementedError):
# smallness parameter for correlated noise source
ff.infidelity(pulse,
spectra[4](S0, omega),
omega,
n_oper_identifiers=['B_0', 'B_2'],
return_smallness=True)
