def test_mutinf_interleave(self):
p = qu.dop(qu.singlet() & qu.singlet())
ixy = qu.mutual_information(p, [2] * 4, sysa=(0, 2))
assert_allclose(ixy, 4)
def test_types(self, k1, k2):
td1 = trace_distance(k1, k2)
td2 = trace_distance(dop(k1), k2)
td3 = trace_distance(k1, dop(k2))
td4 = trace_distance(dop(k1), dop(k2))
assert_allclose([td1] * 3, [td2, td3, td4])
def test_types(self, k1, k2):
td1 = qu.trace_distance(k1, k2)
td2 = qu.trace_distance(qu.dop(k1), k2)
td3 = qu.trace_distance(k1, qu.dop(k2))
td4 = qu.trace_distance(qu.dop(k1), qu.dop(k2))
assert_allclose([td1] * 3, [td2, td3, td4])
def test_evo_timedep_adiabatic_with_callbacks(self, dop, linop,
num_callbacks):
# tests time dependent Evolution via an adiabatic sweep with:
# a) no callbacks
# b) 1 callback that accesses the time-dependent Hamiltonian
# c) 2 callbacks where one access the Hamiltonian and one doesn't
if num_callbacks > 0 and (dop or linop):
# should implement this at some point
return
L = 6
T = 20
H1 = qu.ham_mbl(L, dh=1.0, seed=4, sparse=True)
gs1 = qu.groundstate(H1)
H2 = qu.ham_mbl(L, dh=1.0, seed=5, sparse=True)
gs2 = qu.groundstate(H2)
if linop:
import scipy.sparse.linalg as spla
H1 = spla.aslinearoperator(H1)
H2 = spla.aslinearoperator(H2)
# make sure two ground states are different
assert qu.fidelity(gs1, gs2) < 0.5
# linearly interpolate from one ham to the other
def ham(t):
return (1 - t / T) * H1 + (t / T) * H2
if linop:
assert isinstance(ham(0.3), spla.LinearOperator)
if dop:
p0 = qu.dop(gs1)
else:
p0 = gs1
if num_callbacks == 0:
evo = qu.Evolution(p0, ham, progbar=True)
else:
def gs_overlap(t, pt, H):
evals, evecs = eigs_scipy(H(t), k=1, which='SA')
return np.abs(qu.dot(pt.T, qu.qu(evecs[:, 0])))**2
if num_callbacks == 1:
compute = gs_overlap
if num_callbacks == 2:
def norm(t, pt):
return qu.dot(pt.T, pt)
compute = {'norm': norm, 'gs_overlap': gs_overlap}
evo = qu.Evolution(p0, ham, compute=compute, progbar=True)
evo.update_to(T)
# final state should now overlap much more with second hamiltonian GS
assert qu.fidelity(evo.pt, gs1) < 0.5
assert qu.fidelity(evo.pt, gs2) > 0.99
if num_callbacks == 1:
gs_overlap_results = evo.results
# check that we stayed in the ground state the whole time
assert ((np.array(gs_overlap_results) - 1.0) < 1e-3).all()
if num_callbacks == 2:
norm_results = evo.results['norm']
gs_overlap_results = evo.results['gs_overlap']
# check that we stayed normalized the whole time
assert ((np.array(norm_results) - 1.0) < 1e-3).all()
# check that we stayed in the ground state the whole time
assert ((np.array(gs_overlap_results) - 1.0) < 1e-3).all()
