def test_rand_herm_sparse(self):
a = rand_herm(3, sparse=True, density=0.3)
assert a.shape == (3, 3)
assert type(a) == sp.csr_matrix
assert isherm(a)
evals = np.linalg.eigvals(a.A)
assert_allclose(evals.imag, [0, 0, 0], atol=1e-14)
def test_rand_herm_sparse(self, dtype):
a = qu.rand_herm(3, sparse=True, density=0.3, dtype=dtype)
assert a.shape == (3, 3)
assert type(a) == sp.csr_matrix
assert qu.isherm(a)
assert a.dtype == dtype
evals = qu.eigvalsh(a.A)
assert_allclose(evals.imag, [0, 0, 0], atol=1e-14)
def test_partial_tranpose(self):
p = MPS_rand_state(8, 10)
r = p.ptr([2, 3, 4, 5, 6, 7])
rd = r.to_dense()
assert qu.isherm(rd)
assert qu.ispos(rd)
rpt = r.partial_transpose([0, 1, 2])
rptd = rpt.to_dense()
upper_inds = tuple('b{}'.format(i) for i in range(6))
lower_inds = tuple('k{}'.format(i) for i in range(6))
outer_inds = rpt.outer_inds()
assert all(i in outer_inds for i in upper_inds + lower_inds)
assert qu.isherm(rptd)
assert not qu.ispos(rptd)
def test_entanglement(self):
rho = qu.rand_seperable([2, 3, 2], 10)
assert_allclose(qu.tr(rho), 1.0)
assert qu.isherm(rho)
assert qu.logneg(rho, [2, 6]) < 1e-12
assert qu.logneg(rho, [6, 2]) < 1e-12
rho_a = qu.ptr(rho, [2, 3, 2], 1)
el = qu.eigvalsh(rho_a)
assert np.all(el < 1 - 1e-12)
assert np.all(el > 1e-12)
def test_entanglement(self):
rho = rand_seperable([2, 3, 2], 10)
assert_almost_equal(tr(rho), 1.0)
assert isherm(rho)
assert logneg(rho, [2, 6]) < 1e-12
assert logneg(rho, [6, 2]) < 1e-12
rho_a = ptr(rho, [2, 3, 2], 1)
el = eigvals(rho_a)
assert np.all(el < 1 - 1e-12)
assert np.all(el > 1e-12)
def test_partial_trace(self, rescale):
n = 10
p = MPS_rand_state(n, 7)
r = p.ptr(keep=[2, 3, 4, 6, 8],
upper_ind_id='u{}',
rescale_sites=rescale)
rd = r.to_dense()
if rescale:
assert r.lower_inds == ('u0', 'u1', 'u2', 'u3', 'u4')
assert r.upper_inds == ('k0', 'k1', 'k2', 'k3', 'k4')
else:
assert r.lower_inds == ('u2', 'u3', 'u4', 'u6', 'u8')
assert r.upper_inds == ('k2', 'k3', 'k4', 'k6', 'k8')
assert_allclose(r.trace(), 1.0)
assert isherm(rd)
pd = p.to_dense()
rdd = pd.ptr([2] * n, keep=[2, 3, 4, 6, 8])
assert_allclose(rd, rdd)
def test_partial_trace(self, rescale, keep):
n = 10
p = MPS_rand_state(n, 7)
r = p.ptr(keep=keep, upper_ind_id='u{}', rescale_sites=rescale)
rd = r.to_dense()
if isinstance(keep, slice):
keep = p.slice2sites(keep)
else:
if rescale:
assert r.lower_inds == ('u0', 'u1', 'u2', 'u3', 'u4')
assert r.upper_inds == ('k0', 'k1', 'k2', 'k3', 'k4')
else:
assert r.lower_inds == ('u2', 'u3', 'u4', 'u6', 'u8')
assert r.upper_inds == ('k2', 'k3', 'k4', 'k6', 'k8')
assert_allclose(r.trace(), 1.0)
assert qu.isherm(rd)
pd = p.to_dense()
rdd = pd.ptr([2] * n, keep=keep)
assert_allclose(rd, rdd)
def test_partial_trace_sparse_basic(self):
a = qu.rand_rho(4)
b = qu.partial_trace(a, [2, 2], 0)
assert type(b) == qu.qarray
assert qu.isherm(b)
assert_allclose(qu.tr(b), 1.0)
def test_partial_trace_basic(self):
a = qu.rand_rho(2**2)
b = qu.partial_trace(a, [2, 2], 0)
assert isinstance(b, qu.qarray)
assert qu.isherm(b)
assert_allclose(qu.tr(b), 1.0)
def test_partial_trace_sparse_basic(self):
a = rand_rho(4)
b = partial_trace(a, [2, 2], 0)
assert type(b) == np.matrix
assert isherm(b)
assert_allclose(tr(b), 1.0)
def test_partial_trace_basic(self):
a = rand_rho(2**2)
b = partial_trace(a, [2, 2], 0)
assert isinstance(b, np.matrix)
assert isherm(b)
assert_allclose(tr(b), 1.0)
