def test_ground_state_matches(self, dense, MPO_ham, cyclic):
n = 10
tol = 3e-2 if cyclic else 1e-4
h = MPO_ham(n, cyclic=cyclic)
dmrg = DMRG1(h, bond_dims=[4, 8, 12])
dmrg.opts['local_eig_ham_dense'] = dense
dmrg.opts['periodic_segment_size'] = 1.0
dmrg.opts['periodic_nullspace_fudge_factor'] = 1e-6
assert dmrg.solve(tol=tol / 10, verbosity=1)
assert dmrg.state.cyclic == cyclic
eff_e, mps_gs = dmrg.energy, dmrg.state
mps_gs_dense = mps_gs.to_dense()
assert_allclose(mps_gs_dense.H @ mps_gs_dense, 1.0, rtol=tol)
h_dense = h.to_dense()
# check against dense form
actual_e, gs = eigh(h_dense, k=1)
assert_allclose(actual_e, eff_e, rtol=tol)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0, rtol=tol)
# check against actual MPO_ham
if MPO_ham is MPO_ham_XY:
ham_dense = ham_heis(n, cyclic=cyclic,
j=(1.0, 1.0, 0.0), sparse=True)
elif MPO_ham is MPO_ham_heis:
ham_dense = ham_heis(n, cyclic=cyclic, sparse=True)
actual_e, gs = eigh(ham_dense, k=1)
assert_allclose(actual_e, eff_e, rtol=tol)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0, rtol=tol)
def test_ground_state_matches(self, dense, MPO_ham):
h = MPO_ham(6)
dmrg = DMRG1(h, bond_dims=8)
dmrg.opts['eff_eig_dense'] = dense
assert dmrg.solve()
eff_e, mps_gs = dmrg.energy, dmrg.state
mps_gs_dense = mps_gs.to_dense()
assert_allclose(mps_gs_dense.H @ mps_gs_dense, 1.0)
h_dense = h.to_dense()
# check against dense form
actual_e, gs = seigsys(h_dense, k=1)
assert_allclose(actual_e, eff_e)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0)
# check against actual MPO_ham
if MPO_ham is MPO_ham_XY:
ham_dense = ham_heis(6, cyclic=False, j=(1.0, 1.0, 0.0))
elif MPO_ham is MPO_ham_heis:
ham_dense = ham_heis(6, cyclic=False)
actual_e, gs = seigsys(ham_dense, k=1)
assert_allclose(actual_e, eff_e)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0)
def test_ising_model_with_field(self, cyclic):
p = qtn.MPS_computational_state('0000100000', cyclic=cyclic)
pd = p.to_dense()
H_nni = qtn.NNI_ham_ising(10, j=4, bx=1, cyclic=cyclic)
H_mpo = qtn.MPO_ham_ising(10, j=4, bx=1, cyclic=cyclic)
H = qu.ham_ising(10, jz=4, bx=1, cyclic=cyclic)
tebd = qtn.TEBD(p, H_nni, tol=1e-6)
tebd.split_opts['cutoff'] = 1e-9
tebd.split_opts['cutoff_mode'] = 'rel'
evo = qu.Evolution(pd, H)
e0 = qu.expec(pd, H)
e0_mpo = qtn.expec_TN_1D(p.H, H_mpo, p)
assert e0_mpo == pytest.approx(e0)
tf = 2
ts = np.linspace(0, tf, 21)
evo.update_to(tf)
for pt in tebd.at_times(ts):
assert isinstance(pt, qtn.MatrixProductState)
assert (pt.H @ pt) == pytest.approx(1.0, rel=1e-5)
assert (qu.expec(tebd.pt.to_dense(),
evo.pt) == pytest.approx(1.0, rel=1e-5))
ef_mpo = qtn.expec_TN_1D(tebd.pt.H, H_mpo, tebd.pt)
assert ef_mpo == pytest.approx(e0, 1e-5)
def test_matches_exact(self, dense, MPO_ham):
h = MPO_ham(6)
dmrg = DMRG2(h, bond_dims=8)
assert dmrg._k.site[0].dtype == float
dmrg.opts['eff_eig_dense'] = dense
assert dmrg.solve()
# XXX: need to dispatch SLEPc seigsys on real input
# assert dmrg._k.site[0].dtype == float
eff_e, mps_gs = dmrg.energy, dmrg.state
mps_gs_dense = mps_gs.to_dense()
assert_allclose(mps_gs_dense.H @ mps_gs_dense, 1.0)
h_dense = h.to_dense()
# check against dense form
actual_e, gs = seigsys(h_dense, k=1)
assert_allclose(actual_e, eff_e)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0)
# check against actual MPO_ham
if MPO_ham is MPO_ham_XY:
ham_dense = ham_heis(6, cyclic=False, j=(1.0, 1.0, 0.0))
elif MPO_ham is MPO_ham_heis:
ham_dense = ham_heis(6, cyclic=False)
actual_e, gs = seigsys(ham_dense, k=1)
assert_allclose(actual_e, eff_e)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0)
def test_simple(self):
Z = qu.pauli('Z')
P = qu.projector(Z & Z)
uu = qu.dop(qu.up()) & qu.dop(qu.up())
dd = qu.dop(qu.down()) & qu.dop(qu.down())
assert_allclose(P, uu + dd)
assert qu.expec(P, qu.bell_state('phi+')) == pytest.approx(1.0)
assert qu.expec(P, qu.bell_state('psi+')) == pytest.approx(0.0)
def test_bell_states(self):
for s, dic in zip(("psi-", "psi+", "phi+", "phi-"), ({
"qtype": 'dop'
}, {}, {
"sparse": True
}, {})):
p = bell_state(s, **dic)
assert_allclose(expec(p, p), 1.0)
pa = ptr(p, [2, 2], 0)
assert_allclose(expec(pa, pa), 0.5)
def test_depolarize(self, stack):
rho = qu.rand_rho(2)
I, X, Y, Z = (qu.pauli(s) for s in 'IXYZ')
es = [qu.expec(rho, A) for A in (X, Y, Z)]
p = 0.1
Ek = [(1 - p)**0.5 * I, (p / 3)**0.5 * X, (p / 3)**0.5 * Y,
(p / 3)**0.5 * Z]
if stack:
Ek = np.stack(Ek, axis=0)
sigma = qu.kraus_op(rho, Ek, check=True)
es2 = [qu.expec(sigma, A) for A in (X, Y, Z)]
assert qu.tr(sigma) == pytest.approx(1.0)
assert all(abs(e2) < abs(e) for e, e2 in zip(es, es2))
sig_exp = sum(E @ rho @ qu.dag(E) for E in Ek)
assert_allclose(sig_exp, sigma)
def test_works(self, sz):
prj = zspin_projector(4, sz)
h = ham_heis(4)
h0 = prj @ h @ prj.H
v0s = eigvecs(h0)
for v0 in v0s.T:
vf = prj.H @ v0.T
prjv = vf @ vf.H
# Check reconstructed full eigenvectors commute with full ham
assert_allclose(prjv @ h, h @ prjv, atol=1e-13)
if sz == 0:
# Groundstate must be in most symmetric subspace
gs = groundstate(h)
gs0 = prj .H @ v0s[:, 0]
assert_allclose(expec(gs, gs0), 1.0)
assert_allclose(expec(h, gs0), expec(h, gs))
def test_spin_half_double_space(self, sz):
prj = qu.zspin_projector(5, sz)
h = qu.ham_heis(5)
h0 = prj.T @ h @ prj
v0s = qu.eigvecsh(h0)
for v0 in v0s.T:
vf = prj @ v0.T
prjv = vf @ vf.H
# Check reconstructed full eigenvectors commute with full ham
assert_allclose(prjv @ h, h @ prjv, atol=1e-13)
if sz == 0:
# Groundstate must be in most symmetric subspace
gs = qu.groundstate(h)
gs0 = prj @ v0s[:, 0]
assert_allclose(qu.expec(gs, gs0), 1.0)
assert_allclose(qu.expec(h, gs0), qu.expec(h, gs))
def test_evolve_obc(self, order, dt, tol):
n = 10
tf = 2
psi0 = qtn.MPS_neel_state(n)
H_int = qu.ham_heis(2, cyclic=False)
if dt and tol:
with pytest.raises(ValueError):
qtn.TEBD(psi0, H_int, dt=dt, tol=tol)
return
tebd = qtn.TEBD(psi0, H_int, dt=dt, tol=tol)
tebd.split_opts['cutoff'] = 0.0
if (dt is None and tol is None):
with pytest.raises(ValueError):
tebd.update_to(tf, order=order)
return
tebd.update_to(tf, order=order)
assert tebd.t == approx(tf)
assert not tebd._queued_sweep
dpsi0 = psi0.to_dense()
dham = qu.ham_heis(n=n, sparse=True, cyclic=False)
evo = qu.Evolution(dpsi0, dham)
evo.update_to(tf)
assert qu.expec(evo.pt, tebd.pt.to_dense()) == approx(1, rel=1e-5)
def get_crossing(ghz_state: BaseGHZState, geometry: BaseGeometry, N: int, V: float):
ghz_1 = ghz_state._get_components()[1]
Delta_range = np.linspace(-characteristic_V * 5, characteristic_V * 5, 50)
s_qs = StaticQubitSystem(
N=N, V=V, geometry=geometry,
Omega=0, Delta=Delta_range,
)
energies = []
for detuning in Delta_range:
H = s_qs.get_hamiltonian(detuning)
energy = q.expec(H, ghz_1).real
energies.append(energy)
energies = np.array(energies)
def find_root(x: np.ndarray, y: np.ndarray):
"""
Finds crossing (where y equals 0), given that x, y is roughly linear.
"""
if (y == 0).all():
return np.nan
_right_bound = (y < 0).argmax()
_left_bound = _right_bound - 1
crossing = y[_left_bound] / (y[_left_bound] - y[_right_bound]) \
* (x[_right_bound] - x[_left_bound]) + x[_left_bound]
return crossing
crossing = find_root(Delta_range, energies)
print(f"Found crossing: {crossing:.3e}")
return crossing
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 test_seigsys_slepc_eigvecs(self):
h = rand_herm(100, sparse=True, density=0.2)
lks, vks = seigsys_slepc(h, k=5)
lka, vka = seigsys(h, k=5)
assert vks.shape == vka.shape
for ls, vs, la, va in zip(lks, vks.T, lka, vka.T):
assert_allclose(ls, la)
assert_allclose(expec(vs, va), 1.0)
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_half_filling_groundstate(self):
H = qu.ham_hubbard_hardcore(8, t=0.5, V=1.0, mu=1.0)
gs = qu.groundstate(H)
dims = [2] * 8
cn = qu.num(2)
ens = [qu.expec(cn, qu.ptr(gs, dims, i)) for i in range(8)]
for en in ens:
assert en == pytest.approx(0.5, rel=1e-6)
def test_project_eig(self, backend):
Hl = qu.Lazy(qu.ham_heis, 4, sparse=True, shape=(16, 16))
Pl = qu.Lazy(qu.zspin_projector, 4, shape=(16, 6))
ge, gs = qu.eigh(Hl, P=Pl, k=1, backend=backend)
assert ge == pytest.approx(-2)
assert qu.expec(gs, gs) == pytest.approx(1.0)
def test_works(self, sz):
prj = qu.zspin_projector(4, sz)
h = qu.ham_heis(4)
h0 = prj.T @ h @ prj
v0s = qu.eigvecsh(h0)
for i in range(v0s.shape[1]):
v0 = v0s[:, [i]]
vf = prj @ v0
prjv = vf @ vf.H
# Check reconstructed full eigenvectors commute with full ham
assert_allclose(prjv @ h, h @ prjv, atol=1e-13)
if sz == 0:
# Groundstate must be in most symmetric subspace
gs = qu.groundstate(h)
gs0 = prj @ v0s[:, [0]]
assert_allclose(qu.expec(gs, gs0), 1.0)
assert_allclose(qu.expec(h, gs0), qu.expec(h, gs))
def test_evo_at_times(self):
ham = qu.ham_heis(2, cyclic=False)
p0 = qu.up() & qu.down()
sim = qu.Evolution(p0, ham, method='solve')
ts = np.linspace(0, 10)
for t, pt in zip(ts, sim.at_times(ts)):
x = cos(t)
y = qu.expec(pt, qu.ikron(qu.pauli('z'), [2, 2], 0))
assert_allclose(x, y, atol=1e-15)
def test_quevo_at_times(self):
ham = ham_heis(2, cyclic=False)
p0 = up() & down()
sim = QuEvo(p0, ham, method='solve')
ts = np.linspace(0, 10)
for t, pt in zip(ts, sim.at_times(ts)):
x = cos(t)
y = expec(pt, eyepad(pauli('z'), [2, 2], 0))
assert_allclose(x, y, atol=1e-15)
def state_local_occs(self, qstate=None, k=0, faces=False):
'''
TODO: fix face shapes?
Expectation values <k|local fermi occupation|k>
in the kth excited eigenstate, at vertex
sites (and faces if specified).
param qstate: vector in full qubit basis
param k: if `state` isn't specified, will compute for
the kth excited energy eigenstate (defaults to ground state)
param faces: if True, return occupations at face qubits as well
Returns: local occupations `nocc_v`, (`nocc_f`)
nocc_v (ndarray, shape (Lx,Ly))
nocc_f (ndarray, shape (Lx-1,Ly-1))
'''
if qstate is None:
qstate = self._eigstates[:, k]
#local number ops acting on each site j
# Nj = self.site_number_ops(sites=self.all_sites() , fermi=False)
Nj = [
qu.ikron(self.number_op(), self._sim_dims, [site])
for site in self.all_sites()
]
#expectation <N> for each vertex
nocc_v = np.array([
qu.expec(Nj[v], qstate) for v in self.vertex_sites()
]).reshape(self._lat_shape)
if not faces:
return nocc_v
#expectation <Nj> for each face
nocc_f = np.array([qu.expec(Nj[f], qstate) for f in self.face_sites()])
return nocc_v, nocc_f
def state_occs(self, state):
Nk = [
qu.ikron(number_op(), self._dims, [site])
for site in self._V_ind.flatten()
]
n_ij = np.real(
np.array([qu.expec(Nk[k], state)
for k in range(self._N)])).reshape(self._shape)
return n_ij
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_insert_operator(self):
p = MPS_rand_state(3, 7, tags='KET')
q = p.H.retag({'KET': 'BRA'})
qp = q & p
sz = qu.spin_operator('z').real
qp.insert_operator(sz, ('KET', 'I1'), ('BRA', 'I1'),
tags='SZ', inplace=True)
assert 'SZ' in qp.tags
assert len(qp.tensors) == 7
x1 = qp ^ all
x2 = qu.expec(p.to_dense(), qu.ikron(sz, [2, 2, 2], inds=1))
assert x1 == pytest.approx(x2)
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_prepare_GHZ(self):
qc = qtn.Circuit(3)
gates = [
('H', 0),
('H', 1),
('CNOT', 1, 2),
('CNOT', 0, 2),
('H', 0),
('H', 1),
('H', 2),
]
qc.apply_circuit(gates)
assert qu.expec(qc.psi.to_dense(), qu.ghz_state(3)) == pytest.approx(1)
def test_matches_exact(self, dense, MPO_ham, cyclic):
n = 6
h = MPO_ham(n, cyclic=cyclic)
tol = 3e-2 if cyclic else 1e-4
dmrg = DMRG2(h, bond_dims=[4, 8, 12])
assert dmrg._k[0].dtype == float
dmrg.opts['local_eig_ham_dense'] = dense
dmrg.opts['periodic_segment_size'] = 1.0
dmrg.opts['periodic_nullspace_fudge_factor'] = 1e-6
assert dmrg.solve(tol=tol / 10, verbosity=1)
# XXX: need to dispatch SLEPc eigh on real input
# assert dmrg._k[0].dtype == float
eff_e, mps_gs = dmrg.energy, dmrg.state
mps_gs_dense = mps_gs.to_dense()
assert_allclose(expec(mps_gs_dense, mps_gs_dense), 1.0, rtol=tol)
h_dense = h.to_dense()
# check against dense form
actual_e, gs = eigh(h_dense, k=1)
assert_allclose(actual_e, eff_e, rtol=tol)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0, rtol=tol)
# check against actual MPO_ham
if MPO_ham is MPO_ham_XY:
ham_dense = ham_heis(n, cyclic=cyclic, j=(1.0, 1.0, 0.0))
elif MPO_ham is MPO_ham_heis:
ham_dense = ham_heis(n, cyclic=cyclic)
actual_e, gs = eigh(ham_dense, k=1)
assert_allclose(actual_e, eff_e, rtol=tol)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0, rtol=tol)
def test_compute_local_expectation_vs_dense(self, Lx, Ly, normalized):
# (2Lx-1) * (2Ly-1) lattice ePEPSvector
epeps = qnets.QubitEncodeVector.rand(Lx, Ly)\
.convert_to_ePEPS_vector()
# qubits + dummies
n_sites = (2 * Lx - 1) * (2 * Ly - 1)
# vertices + occupied faces in 'original' Lx*Ly lattice
n_qubits = (Lx * Ly) + int((Lx - 1) * (Ly - 1) / 2)
# 'qubit' Fermi-Hubbard with random parameters
t, V, mu = np.random.rand(3)
H = hams.SpinlessSimHam(Lx, Ly, t, V, mu)
# separate indices of qubits from 'aux' tensors
qubit_inds = tuple(
starmap(epeps.site_ind,
(epeps.qubit_to_coo_map[q] for q in range(n_qubits))))
non_qubit_inds = set(epeps.site_inds) - set(qubit_inds)
# 'densify' into vector with qubit dimensions first
psi_dense = epeps.to_dense((*qubit_inds, *non_qubit_inds))
if normalized:
qu.normalize(psi_dense)
dense_terms = [
qu.pkron(term, dims=[2] * n_sites, inds=where)
for where, term in H.gen_ham_terms()
]
exact = sum((qu.expec(h, psi_dense) for h in dense_terms))
# now compute energy with `ePEPSvector.compute_local_expectation`
q2coo = lambda q: epeps.qubit_to_coo_map[q]
CooHam = H.convert_to_coordinate_ham(q2coo)
terms = CooHam._coo_ham_terms
envs, plaqmap = epeps.calc_plaquette_envs_and_map(terms)
opts = dict(cutoff=2e-3, max_bond=9, contract_optimize='random-greedy')
e = epeps.compute_local_expectation(terms,
normalized=normalized,
autogroup=False,
plaquette_envs=envs,
plaquette_map=plaqmap,
**opts)
assert e == pytest.approx(exact, rel=1e-2)
def test_NNI_and_single_site_terms_heis(self):
n = 10
psi0 = qtn.MPS_neel_state(n)
H_nni = qtn.NNI_ham_heis(n, j=(0.7, 0.8, 0.9), bz=0.337)
tebd = qtn.TEBD(psi0, H_nni)
tebd.update_to(1.0, tol=1e-5)
assert abs(psi0.H @ tebd.pt) < 1.0
assert tebd.pt.entropy(5) > 0.0
psi0_dns = qu.neel_state(n)
H_dns = qu.ham_heis(10, j=(0.7, 0.8, 0.9), b=0.337, cyclic=False)
evo = qu.Evolution(psi0_dns, H_dns)
evo.update_to(1.0)
assert qu.expec(tebd.pt.to_dense(), evo.pt) == pytest.approx(1.0)
def test_ising_and_MPS_product_state(self):
h = MPO_ham_ising(6, bx=2.0, j=0.1)
dmrg = DMRG1(h, bond_dims=8)
assert dmrg.solve(verbosity=1)
eff_e, mps_gs = dmrg.energy, dmrg.state
mps_gs_dense = mps_gs.to_dense()
assert_allclose(mps_gs_dense.H @ mps_gs_dense, 1.0)
# check against dense
h_dense = h.to_dense()
actual_e, gs = eigh(h_dense, k=1)
assert_allclose(actual_e, eff_e)
assert_allclose(abs(expec(mps_gs_dense, gs)), 1.0)
exp_gs = MPS_product_state([plus()] * 6)
assert_allclose(abs(exp_gs.H @ mps_gs), 1.0, rtol=1e-3)
def test_NNI_and_single_site_terms(self):
n = 10
psi0 = qtn.MPS_neel_state(n)
H_nni = qtn.NNI_ham_XY(n, bz=0.9)
assert H_nni.special_sites == {(8, 9)}
tebd = qtn.TEBD(psi0, H_nni)
tebd.update_to(1.0, tol=1e-5)
assert abs(psi0.H @ tebd.pt) < 1.0
assert tebd.pt.entropy(5) > 0.0
psi0_dns = qu.neel_state(n)
H_dns = qu.ham_XY(10, jxy=1.0, bz=0.9, cyclic=False)
evo = qu.Evolution(psi0_dns, H_dns)
evo.update_to(1.0)
assert qu.expec(tebd.pt.to_dense(), evo.pt) == pytest.approx(1.0)
