def test_kron_basic(self, parallel):
a = qu.rand_ket(2)
b = qu.rand_ket(4)
c = qu.rand_ket(4)
d = qu.rand_ket(5)
t = qu.kron(a, b, c, d, parallel=parallel)
assert_allclose(t, a & b & c & d)
def test_bell_state(self):
p = qu.bell_state('psi-')
assert_allclose(qu.schmidt_gap(p, [2, 2], 0), 0.0)
p = qu.up() & qu.down()
assert_allclose(qu.schmidt_gap(p, [2, 2], 0), 1.0)
p = qu.rand_ket(2**3)
assert 0 < qu.schmidt_gap(p, [2] * 3, sysa=[0, 1]) < 1.0
def test_bell_state(self):
p = bell_state('psi-')
assert_allclose(schmidt_gap(p, [2, 2], 0), 0.0)
p = up() & down()
assert_allclose(schmidt_gap(p, [2, 2], 0), 1.0)
p = rand_ket(2**3)
assert 0 < schmidt_gap(p, [2] * 3, sysa=[0, 1]) < 1.0
def test_quantum_discord_pure(self):
for _ in range(10):
p = qu.rand_ket(4)
p = p @ p.H
iab = qu.mutual_information(p)
qd = qu.quantum_discord(p)
assert_allclose(iab / 2, qd)
def test_from_dense(self):
psi = qu.rand_ket(2**8)
mps = MatrixProductState.from_dense(psi, dims=[2] * 8)
assert mps.tags == {'I{}'.format(i) for i in range(8)}
assert mps.site_inds == tuple('k{}'.format(i) for i in range(8))
assert mps.nsites == 8
mpod = mps.to_dense()
assert qu.expec(mpod, psi) == pytest.approx(1)
def test_bigger(self):
psi = qu.rand_ket(2**5)
assert np.sum(abs(psi) < 1e-12) == 0
A = qu.kronpow(qu.pauli('Z'), 5)
res, psi_after = qu.measure(psi, A, eigenvalue=-1.0)
# should have projected to half subspace
assert np.sum(abs(psi_after) < 1e-12) == 2**4
assert res == -1.0
def test_vec_dense(self):
a = qu.rand_ket(4)
b = qu.core._trace_keep(a, [2, 2], 0)
c = qu.partial_trace(a.A, [2, 2], 0)
assert_allclose(b, c)
b = qu.core._trace_keep(a, [2, 2], 1)
c = qu.partial_trace(a.A, [2, 2], 1)
assert_allclose(b, c)
def test_vec_dense(self):
a = rand_ket(4)
b = _trace_lose(a, [2, 2], 1)
c = partial_trace(a.A, [2, 2], 0)
assert_allclose(b, c)
b = _trace_lose(a, [2, 2], 0)
c = partial_trace(a.A, [2, 2], 1)
assert_allclose(b, c)
def test_mutinf_subsys_pure(self):
p = rand_ket(2**7)
dims = (2**3, 2**4)
# exact
mi0 = mutual_information(p, dims, sysa=0)
mi1 = mutinf_subsys(p, dims, sysa=0, sysb=1, approx_thresh=1e30)
assert_allclose(mi1, mi0)
# approx
mi2 = mutinf_subsys(p, dims, sysa=0, sysb=1, approx_thresh=1)
assert_allclose(mi1, mi2, rtol=5e-2)
def test_entropy_subsystem(self):
p = rand_ket(2**9)
# exact
e1 = entropy_subsys(p, (2**5, 2**4), 0, approx_thresh=1e30)
# approx
e2 = entropy_subsys(p, (2**5, 2**4), 0, approx_thresh=1)
assert e1 != e2
assert_allclose(e1, e2, rtol=5e-2)
assert entropy_subsys(p, (2**5, 2**4), [0, 1], approx_thresh=1) == 0.0
def test_expm_slepc(self, expm_backend):
ham = qu.ham_mbl(7, dh=0.5, sparse=True)
psi = qu.rand_ket(2**7)
evo_exact = qu.Evolution(psi, ham, method='solve')
evo_slepc = qu.Evolution(psi,
ham,
method='expm',
expm_backend=expm_backend)
ts = np.linspace(0, 100, 6)
for p1, p2 in zip(evo_exact.at_times(ts), evo_slepc.at_times(ts)):
assert abs(qu.expec(p1, p2) - 1) < 1e-9
def test_mutinf_subsys(self):
p = qu.rand_ket(2**9)
dims = (2**3, 2**2, 2**4)
# exact
rho_ab = qu.ptr(p, dims, [0, 2])
mi0 = qu.mutual_information(rho_ab, [8, 16])
mi1 = qu.mutinf_subsys(p, dims, sysa=0, sysb=2, approx_thresh=1e30)
assert_allclose(mi1, mi0)
# approx
mi2 = qu.mutinf_subsys(p, dims, sysa=0, sysb=2, approx_thresh=1)
assert_allclose(mi1, mi2, rtol=0.1)
def test_exp_sparse(self):
a = rand_herm(100, sparse=True, density=0.1)
k = rand_ket(100)
out = mfn_multiply_slepc(a, k)
al, av = eigh(a.A)
expected = av @ np.diag(np.exp(al)) @ av.conj().T @ k
assert_allclose(out, expected)
def test_construct(self, gate, dtype, sparse):
if gate in ('Rx', 'Ry', 'Rz', 'phase_gate'):
args = (0.43827, )
elif gate in ('U_gate'):
args = (0.1, 0.2, 0.3)
else:
args = ()
G = getattr(qu, gate)(*args, dtype=dtype, sparse=sparse)
assert G.dtype == dtype
assert qu.issparse(G) is sparse
psi = qu.rand_ket(G.shape[0])
Gpsi = G @ psi
assert qu.expec(Gpsi, Gpsi) == pytest.approx(1.0)
def test_logneg_subsys_pure_should_swap_subsys(self):
p = qu.rand_ket(2**(5 + 2))
dims = (2**5, 2**2)
sysa = 0
sysb = 1
# exact 1
ln0 = qu.logneg(p, dims, 0)
# exact 2
ln1 = qu.logneg_subsys(p, dims, sysa, sysb, approx_thresh=1e30)
assert_allclose(ln0, ln1)
# approx
ln2 = qu.logneg_subsys(p, dims, sysa, sysb, approx_thresh=1, tol=0.005)
assert ln1 != ln2
assert_allclose(ln1, ln2, rtol=0.2)
def test_logneg_subsys_pure(self):
p = qu.rand_ket(2**(3 + 4))
dims = (2**3, 2**4)
sysa = 0
sysb = 1
# exact 1
ln0 = qu.logneg(p, dims, 0)
# exact 2
ln1 = qu.logneg_subsys(p, dims, sysa, sysb, approx_thresh=1e30)
assert_allclose(ln0, ln1)
# approx
ln2 = qu.logneg_subsys(p, dims, sysa, sysb, approx_thresh=1, tol=5e-3)
assert ln1 != ln2
assert_allclose(ln1, ln2, rtol=1e-1)
def test_logneg_subsys(self):
p = qu.rand_ket(2**(2 + 3 + 1 + 2))
dims = (2**2, 2**3, 2**1, 2**2)
sysa = [0, 3]
sysb = 1
# exact 1
ln0 = qu.logneg(qu.ptr(p, dims, [0, 1, 3]), [4, 8, 4], [0, 2])
# exact 2
ln1 = qu.logneg_subsys(p, dims, sysa, sysb, approx_thresh=1e30)
assert_allclose(ln0, ln1)
# approx
ln2 = qu.logneg_subsys(p, dims, sysa, sysb, approx_thresh=1)
assert ln1 != ln2
assert_allclose(ln1, ln2, rtol=5e-2)
def test_sqrt_sparse(self):
import scipy.sparse as sp
a = rand_pos(32, sparse=True, density=0.1)
a = a + 0.001 * sp.eye(32)
k = rand_ket(32)
out = mfn_multiply_slepc(a, k, fntype='sqrt', isherm=True)
al, av = eigh(a.A)
al[al < 0] = 0.0 # very small neg values spoil sqrt
expected = av @ np.diag(np.sqrt(al)) @ av.conj().T @ k
assert_allclose(out, expected, rtol=1e-6)
def test_expm_krylov_expokit(self):
ham = rand_herm(100, sparse=True, density=0.8)
psi = rand_ket(100)
evo_exact = QuEvo(psi, ham, method='solve')
evo_krylov = QuEvo(psi,
ham,
method='expm',
expm_backend='slepc-krylov')
evo_expokit = QuEvo(psi,
ham,
method='expm',
expm_backend='slepc-expokit')
ts = np.linspace(0, 100, 21)
for p1, p2, p3 in zip(evo_exact.at_times(ts), evo_krylov.at_times(ts),
evo_expokit.at_times(ts)):
assert abs(expec(p1, p2) - 1) < 1e-9
assert abs(expec(p1, p3) - 1) < 1e-9
def ham_rcr_psi():
# Define a random hamiltonian with a known recurrence time
d = 3
np.random.seed(1)
ems = np.random.randint(1, 6, d)
ens = np.random.randint(1, 6, d) # eigenvalues as rational numbers
# numerator lowest common divisor
LCD = reduce(gcd, ems)
# denominator lowest common multiple
LCM = reduce(lambda a, b: a * b // gcd(a, b), ens)
trc = 2 * pi * LCM / LCD
evals = np.array(ems) / np.array(ens)
v = rand_uni(d)
ham = v @ np.diag(evals) @ v.H
p0 = rand_ket(d)
tm = 0.573 * trc
pm = v @ np.diag(np.exp(-1.0j * tm * evals)) @ v.H @ p0
return ham, trc, p0, tm, pm
def test_swap_higher_dim(self, sparse):
a = qu.rand_ket(9)
s = qu.swap(3, sparse=sparse)
assert_allclose(s @ a, a.reshape([3, 3]).T.reshape([9, 1]))
def psi_mb_ab_mat():
return np.concatenate(
[rand_ket(prod(DIMS_MB[:7])) for _ in range(6)],
axis=1)
def psi_mb_ab():
return rand_ket(prod(DIMS_MB[:7]))
def psi_mb_abc():
return rand_ket(prod(DIMS_MB))
def psi_ab_mat():
return np.concatenate(
[rand_ket(prod(DIMS[:-1])) for _ in range(6)],
axis=1)
def psi_ab():
return rand_ket(prod(DIMS[:-1]))
def psi_abc():
return rand_ket(prod(DIMS))
def ket_d2():
return rand_ket(_TEST_SZ)
def test_permute_sparse_ket(self):
dims = [3, 2, 5, 4]
a = qu.rand_ket(qu.prod(dims), sparse=True, density=0.5)
b = qu.permute(a, dims, [3, 1, 2, 0])
c = qu.permute(a.A, dims, [3, 1, 2, 0])
assert_allclose(b.A, c)
def test_partial_trace_simple_ket(self):
a = qu.rand_ket(12, sparse=True, density=0.5)
dims = [2, 3, 2]
b = qu.partial_trace(a, dims, [0, 1])
c = qu.partial_trace(a.A, dims, [0, 1])
assert_allclose(b, c)
