def check_distance(a: Mps, b: Mps):
d1 = (a - b).dmrg_norm
d2 = a.distance(b)
a_array = a.full_wfn().array
b_array = b.full_wfn().array
d3 = np.linalg.norm(a_array - b_array)
assert d1 == pytest.approx(d2) == pytest.approx(d3)
def optimize_mps(mps: Mps, mpo: Mpo):
energies = optimize_mps_dmrg(mps, mpo)
if not mps.hybrid_tdh:
return energies[-1]
# from matplotlib import pyplot as plt
# plt.plot(energies); plt.show()
HAM = []
for mol in mps.mol_list:
for ph in mol.hartree_phs:
HAM.append(ph.h_indep)
optimize_mps_hartree(mps, HAM)
for itera in range(mps.optimize_config.niterations):
logging.info("Loop: %d" % itera)
MPO, HAM, Etot = mps.construct_hybrid_Ham(mpo)
MPS_old = mps.copy()
optimize_mps_dmrg(mps, MPO)
optimize_mps_hartree(mps, HAM)
# check convergence
dmrg_converge = abs(mps.angle(MPS_old) -
1) < mps.optimize_config.dmrg_thresh
hartree_converge = np.all(
mps.hartree_wfn_diff(MPS_old) < mps.optimize_config.hartree_thresh)
if dmrg_converge and hartree_converge:
logger.info("SCF converge!")
break
return Etot
def test_distance():
model = custom_model(n_phys_dim=(2, 2))
a = Mps.random(model, 1, 10)
b = Mps.random(model, 1, 10)
check_distance(a, b)
h = Mpo(model)
for i in range(100):
a = a.evolve(h, 10)
b = b.evolve(h, 10)
check_distance(a, b)
def test_distance():
mol_list = parameter.custom_mol_list(n_phys_dim=(2, 2))
a = Mps.random(mol_list, 1, 10)
b = Mps.random(mol_list, 1, 10)
check_distance(a, b)
h = Mpo(mol_list)
for i in range(100):
a = a.evolve(h, 10)
b = b.evolve(h, 10)
check_distance(a, b)
def test_save_load():
mps = Mpo.onsite(parameter.mol_list, "a^\dagger",
mol_idx_set={0}).apply(Mps.gs(parameter.mol_list, False))
mpo = Mpo(parameter.mol_list)
mps1 = mps.copy()
for i in range(2):
mps1 = mps1.evolve(mpo, 10)
mps2 = mps.evolve(mpo, 10)
fname = "test.npz"
mps2.dump(fname)
mps2 = Mps.load(parameter.mol_list, fname)
mps2 = mps2.evolve(mpo, 10)
assert np.allclose(mps1.e_occupations, mps2.e_occupations)
os.remove(fname)
def test_save_load():
model = holstein_model
mps = Mpo.onsite(model, r"a^\dagger", dof_set={0}) @ Mps.ground_state(
model, False)
mpo = Mpo(model)
mps1 = mps.copy()
for i in range(2):
mps1 = mps1.evolve(mpo, 10)
mps2 = mps.evolve(mpo, 10)
fname = "test.npz"
mps2.dump(fname)
mps2 = Mps.load(model, fname)
mps2 = mps2.evolve(mpo, 10)
assert np.allclose(mps1.e_occupations, mps2.e_occupations)
os.remove(fname)
def test_save_load():
mol_list = custom_mol_list(hartrees=[True, False])
mps = Mpo.onsite(mol_list, "a^\dagger", mol_idx_set={0}) @ Mps.gs(
mol_list, False)
mpo = Mpo(mol_list)
mps1 = mps.copy()
for i in range(2):
mps1 = mps1.evolve(mpo, 10)
mps2 = mps.evolve(mpo, 10)
fname = "test.npz"
mps2.dump(fname)
mps2 = Mps.load(mol_list, fname)
mps2 = mps2.evolve(mpo, 10)
assert np.allclose(mps1.e_occupations, mps2.e_occupations)
os.remove(fname)
def find_highest_energy(h_mpo: Mpo, nexciton, Mmax):
logger.debug("begin finding highest energy")
model = h_mpo.model
mps = Mps.random(model, nexciton, Mmax)
mps.optimize_config.inverse = -1.0
energies, _ = optimize_mps(mps, h_mpo)
return -energies[-1]
def test_svd_compress(comp, mp):
if mp == "mpo":
mps = Mpo(holstein_model)
M = 22
else:
mps = Mps.random(holstein_model, 1, 10)
if mp == "mpdm":
mps = MpDm.from_mps(mps)
mps.canonicalise().normalize()
M = 36
if comp:
mps = mps.to_complex(inplace=True)
print(f"{mps}")
mpo = Mpo(holstein_model)
if comp:
mpo = mpo.scale(-1.0j)
print(f"{mpo.bond_dims}")
std_mps = mpo.apply(mps, canonicalise=True).canonicalise()
print(f"std_mps: {std_mps}")
mps.compress_config.bond_dim_max_value = M
mps.compress_config.criteria = CompressCriteria.fixed
svd_mps = mpo.contract(mps)
dis = svd_mps.distance(std_mps) / std_mps.dmrg_norm
print(f"svd_mps: {svd_mps}, dis: {dis}")
assert np.allclose(dis, 0.0, atol=1e-3)
assert np.allclose(svd_mps.dmrg_norm, std_mps.dmrg_norm, atol=1e-4)
def init_mps(self):
mmax = self.optimize_config.procedure[0][0]
i_mps = Mps.random(self.h_mpo.model, self.nexciton, mmax, 1)
i_mps.optimize_config = self.optimize_config
energy, i_mps = gs.optimize_mps(i_mps, self.h_mpo)
if self.spectratype == "emi":
operator = "a"
else:
operator = r"a^\dagger"
dipole_mpo = Mpo.onsite(self.model, operator, dipole=True)
if self.temperature != 0:
beta = self.temperature.to_beta()
# print "beta=", beta
# thermal_mpo = Mpo.exact_propagator(self.model, -beta / 2.0, space=self.space1, shift=self.shift1)
# ket_mps = thermal_mpo.apply(i_mps)
# ket_mps.normalize()
# no test, don't know work or not
i_mpdm = MpDm.from_mps(i_mps)
tp = ThermalProp(i_mpdm, self.h_mpo, exact=True, space=self.space1)
tp.evolve(None, 1, beta / 2j)
ket_mps = tp.latest_mps
else:
ket_mps = i_mps
a_ket_mps = dipole_mpo.apply(ket_mps, canonicalise=True)
a_ket_mps.canonical_normalize()
if self.temperature != 0:
a_bra_mps = ket_mps.copy()
else:
a_bra_mps = a_ket_mps.copy()
return BraKetPair(a_bra_mps, a_ket_mps)
def test_clear():
gs_mps = Mps.gs(mol_list, max_entangled=False)
mps = creation_operator.apply(gs_mps)
new_mps = mps.copy()
new_mps.clear_memory()
assert new_mps.total_bytes < mps.total_bytes
check_property(new_mps)
def test_expectations(mpos):
random = Mps.random(parameter.holstein_model, 1, 20)
e1 = random.expectations(mpos)
e2 = random.expectations(mpos, opt=False)
assert np.allclose(e1, e2)
def init_mps(self):
tentative_mpo = Mpo(self.mol_list)
if self.temperature == 0:
gs_mp = Mps.gs(self.mol_list, max_entangled=False)
if self.dissipation != 0:
gs_mp = MpDm.from_mps(gs_mp)
else:
gs_mp = MpDm.max_entangled_gs(self.mol_list)
# subtract the energy otherwise might cause numeric error because of large offset * dbeta
energy = Quantity(gs_mp.expectation(tentative_mpo))
mpo = Mpo(self.mol_list, offset=energy)
tp = ThermalProp(gs_mp, mpo, exact=True, space="GS")
tp.evolve(None, len(gs_mp), self.temperature.to_beta() / 2j)
gs_mp = tp.latest_mps
init_mp = self.create_electron(gs_mp)
if self.dissipation != 0:
init_mp = MpDmFull.from_mpdm(init_mp)
energy = Quantity(init_mp.expectation(tentative_mpo))
self.mpo = Mpo(self.mol_list, offset=energy)
logger.info(f"mpo bond dims: {self.mpo.bond_dims}")
logger.info(f"mpo physical dims: {self.mpo.pbond_list}")
if self.dissipation != 0:
self.mpo = SuperLiouville(self.mpo, self.dissipation)
init_mp.canonicalise()
init_mp.evolve_config = self.evolve_config
# init the compress config if not using threshold and not set
if self.compress_config.criteria is not CompressCriteria.threshold\
and self.compress_config.max_dims is None:
self.compress_config.set_bonddim(length=len(init_mp) + 1)
init_mp.compress_config = self.compress_config
# init_mp.invalidate_cache()
return init_mp
def test_pyr_4mode(multi_e, translator):
order, basis, vibronic_model = construct_vibronic_model(multi_e)
if translator is ModelTranslator.vibronic_model:
model = vibronic_model
elif translator is ModelTranslator.general_model:
model = vibronic_to_general(vibronic_model)
else:
assert False
mol_list2 = MolList2(order, basis, model, model_translator=translator)
mpo = Mpo(mol_list2)
logger.info(f"mpo_bond_dims:{mpo.bond_dims}")
mps = Mps.hartree_product_state(mol_list2, condition={"e_1": 1})
# for multi-e case the `expand bond dimension` routine is currently not working
# because creation operator is not defined yet
mps.use_dummy_qn = True
mps.build_empty_qn()
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=10)
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps)
job = VibronicModelDynamics(mol_list2,
mps0=mps,
h_mpo=mpo,
compress_config=compress_config,
evolve_config=evolve_config)
time_step_fs = 2
job.evolve(evolve_dt=time_step_fs * fs2au, nsteps=59)
from renormalizer.vibronic.tests.mctdh_data import mctdh_data
assert np.allclose(mctdh_data[::round(time_step_fs / 0.5)][:, 1:],
job.e_occupations_array,
atol=5e-2)
def init_mps(self):
tentative_mpo = Mpo(self.model)
if self.temperature == 0:
gs_mp = Mps.ground_state(self.model, max_entangled=False)
else:
if self._defined_output_path:
gs_mp = load_thermal_state(self.model, self._thermal_dump_path)
else:
gs_mp = None
if gs_mp is None:
gs_mp = MpDm.max_entangled_gs(self.model)
# subtract the energy otherwise might cause numeric error because of large offset * dbeta
energy = Quantity(gs_mp.expectation(tentative_mpo))
mpo = Mpo(self.model, offset=energy)
tp = ThermalProp(gs_mp, mpo, exact=True, space="GS")
tp.evolve(None, max(20, len(gs_mp)), self.temperature.to_beta() / 2j)
gs_mp = tp.latest_mps
if self._defined_output_path:
gs_mp.dump(self._thermal_dump_path)
init_mp = self.create_electron(gs_mp)
energy = Quantity(init_mp.expectation(tentative_mpo))
self.mpo = Mpo(self.model, offset=energy)
logger.info(f"mpo bond dims: {self.mpo.bond_dims}")
logger.info(f"mpo physical dims: {self.mpo.pbond_list}")
init_mp.evolve_config = self.evolve_config
init_mp.compress_config = self.compress_config
if self.evolve_config.is_tdvp:
init_mp = init_mp.expand_bond_dimension(self.mpo)
init_mp.canonicalise()
return init_mp
def test_expectations(mpos):
random = Mps.random(parameter.mol_list, 1, 20)
e1 = random.expectations(mpos)
e2 = random.expectations(mpos, opt=False)
assert np.allclose(e1, e2)
def load_wfn(self, model):
r"""Load tda wavefunction
"""
mps_l_cano = Mps.load(model, "mps_l_cano.npz")
mps_r_cano = Mps.load(model, "mps_r_cano.npz")
tangent_u_dict = np.load("tangent_u.npz")
tangent_u = [tangent_u_dict[str(i)] if str(i) in tangent_u_dict.keys()
else None for i in range(mps_l_cano.site_num)]
tda_coeff_list = []
for iroot in range(self.nroots):
tda_coeff_dict = np.load(f"tda_coeff_{iroot}.npz")
tda_coeff = [tda_coeff_dict[str(i)] if str(i) in tda_coeff_dict.keys()
else None for i in range(mps_l_cano.site_num)]
tda_coeff_list.append(tda_coeff)
self.wfn = [mps_l_cano, mps_r_cano, tangent_u, tda_coeff_list]
def f(mol_list, run_qutip=True):
tentative_mpo = Mpo(mol_list)
init_mps = (Mpo.onsite(mol_list, r"a^\dagger", mol_idx_set={0}) @ Mps.gs(
mol_list, False)).expand_bond_dimension(hint_mpo=tentative_mpo)
init_mpdm = MpDm.from_mps(init_mps).expand_bond_dimension(
hint_mpo=tentative_mpo)
e = init_mps.expectation(tentative_mpo)
mpo = Mpo(mol_list, offset=Quantity(e))
if run_qutip:
# calculate result in ZT. FT result is exactly the same
TIME_LIMIT = 10
QUTIP_STEP = 0.01
N_POINTS = TIME_LIMIT / QUTIP_STEP + 1
qutip_time_series = np.linspace(0, TIME_LIMIT, N_POINTS)
init = qutip.Qobj(init_mps.full_wfn(),
[qutip_h.dims[0], [1] * len(qutip_h.dims[0])])
# the result is not exact and the error scale is approximately 1e-5
res = qutip.sesolve(qutip_h - e,
init,
qutip_time_series,
e_ops=[c.dag() * c for c in qutip_clist])
qutip_expectations = np.array(res.expect).T
return qutip_expectations, QUTIP_STEP, init_mps, init_mpdm, mpo
else:
return init_mps, init_mpdm, mpo
def test_from_mps():
gs = Mps.random(parameter.holstein_model, 1, 20)
gs_mpdm = MpDm.from_mps(gs)
assert np.allclose(gs.e_occupations, gs_mpdm.e_occupations)
gs = gs.canonicalise()
gs_mpdm = gs_mpdm.canonicalise()
assert np.allclose(gs.e_occupations, gs_mpdm.e_occupations)
def test_pyr_4mode(multi_e, dvr):
basis, ham_terms = construct_vibronic_model(multi_e, dvr)
model = Model(basis, ham_terms)
mpo = Mpo(model)
logger.info(f"mpo_bond_dims:{mpo.bond_dims}")
# same form whether multi_e is True or False
init_condition = {"s2": 1}
if dvr:
for dof in model.v_dofs:
idx = model.order[dof]
init_condition[dof] = basis[idx].dvr_v[0]
mps = Mps.hartree_product_state(model, condition=init_condition)
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=10)
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps)
job = VibronicModelDynamics(model,
mps0=mps,
h_mpo=mpo,
compress_config=compress_config,
evolve_config=evolve_config,
expand=True)
time_step_fs = 2
job.evolve(evolve_dt=time_step_fs * fs2au, nsteps=60)
from renormalizer.vibronic.tests.mctdh_data import mctdh_data
assert np.allclose(mctdh_data[::round(time_step_fs / 0.5)][:61, 1:],
job.e_occupations_array,
atol=2e-2)
def init_b_mps(self):
# get the right hand site vector b, Ax=b
# b = -eta * dipole * \psi_0
# only support Holstine model 0/1 exciton manifold
if self.spectratype == "abs":
nexciton = 0
dipoletype = r"a^\dagger"
elif self.spectratype == "emi":
nexciton = 1
dipoletype = "a"
# procedure for ground state calculation
if self.procedure_gs is None:
self.procedure_gs = \
[[10, 0.4], [20, 0.2], [30, 0.1], [40, 0], [40, 0]]
# ground state calculation
mps = Mps.random(
self.model, nexciton, self.procedure_gs[0][0], percent=1.0)
mps.optimize_config = OptimizeConfig(procedure=self.procedure_gs)
mps.optimize_config.method = "2site"
energies, mps = gs.optimize_mps(mps, self.h_mpo)
e0 = min(energies)
dipole_mpo = \
Mpo.onsite(
self.model, dipoletype, dipole=True
)
b_mps = dipole_mpo.apply(mps.scale(-self.eta))
return b_mps, e0
def check_reduced_density_matrix(basis):
model = Model(basis, [])
mps = Mps.random(model, 1, 20)
rdm = mps.calc_edof_rdm().real
assert np.allclose(np.diag(rdm), mps.e_occupations)
# only test a sample. Should be enough.
mpo = Mpo(model, Op(r"a^\dagger a", [0, 3]))
assert rdm[-1][0] == pytest.approx(mps.expectation(mpo))
def test_zt_init_state():
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 10)
mol_list = MolList([Mol(Quantity(0), [ph])], Quantity(0), scheme=3)
mpo = Mpo(mol_list)
mps = Mps.random(mol_list, 1, 10)
optimize_mps(mps, mpo)
ct = ChargeTransport(mol_list)
assert mps.angle(ct.latest_mps) == pytest.approx(1)
def find_lowest_energy(h_mpo: Mpo, nexciton, Mmax, with_hartree=True):
logger.debug("begin finding lowest energy")
if with_hartree:
mol_list = h_mpo.mol_list
else:
mol_list = h_mpo.mol_list.get_pure_dmrg_mollist()
mps = Mps.random(mol_list, nexciton, Mmax)
energy = optimize_mps(mps, h_mpo)
return energy.min()
def init_cv_mps(self):
# random guess of cv_mps with same qn as b_mps
assert self.b_mps is not None
# initialize guess of cv_mps
cv_mps = Mps.random(
self.model, self.b_mps.qntot, self.m_max, percent=1.0)
logger.info(f"cv_mps random guess qntot: {cv_mps.qntot}")
return cv_mps
def test_environ():
mps = Mps.random(holstein_model, 1, 10)
mpo = Mpo(holstein_model)
mps = mps.evolve(mpo, 10)
environ = Environ(mps, mpo)
for i in range(len(mps) - 1):
l = environ.read("L", i)
r = environ.read("R", i + 1)
e = complex(tensordot(l, r, axes=((0, 1, 2), (0, 1, 2)))).real
assert pytest.approx(e) == mps.expectation(mpo)
def max_entangled_ex(cls, mol_list, normalize=True):
"""
T = \\infty maximum entangled EX state
"""
mps = Mps.gs(mol_list, max_entangled=True)
# the creation operator \\sum_i a^\\dagger_i
ex_mps = Mpo.onsite(mol_list, r"a^\dagger").apply(mps)
if normalize:
ex_mps.normalize(1.0)
return cls.from_mps(ex_mps)
def test_phonon_onsite():
gs = Mps.gs(mol_list, max_entangled=False)
assert not gs.ph_occupations.any()
b2 = Mpo.ph_onsite(mol_list, r"b^\dagger", 0, 0)
p1 = b2.apply(gs).normalize()
assert np.allclose(p1.ph_occupations, [1, 0, 0, 0, 0, 0])
p2 = b2.apply(p1).normalize()
assert np.allclose(p2.ph_occupations, [2, 0, 0, 0, 0, 0])
b = b2.conj_trans()
assert b.distance(Mpo.ph_onsite(mol_list, r"b", 0, 0)) == 0
assert b.apply(p2).normalize().distance(p1) == pytest.approx(0, abs=1e-5)
def test_csvd():
np.random.seed(0)
mps1, mpo = construct_mps_mpo_2(mol_list, procedure[0][0], nexciton)
mps1.threshold = 1e-6
mps1.optimize_config.procedure = procedure
optimize_mps(mps1, mpo)
mps1.compress()
mps2 = Mps.load(mol_list, os.path.join(cur_dir, "test_svd_qn.npz"))
d = pytest.approx(mps1.distance(mps2), abs=1e-4)
# the same direction or opposite direction
assert d == 0 or d == 2
def test_displacement():
def get_e_occu(idx):
res = np.zeros(len(mol_list))
res[idx] = 1
return res
gs = Mps.gs(mol_list, max_entangled=False)
gs = Mpo.onsite(mol_list, r"a^\dagger", mol_idx_set={0}).apply(gs).compress()
assert np.allclose(gs.e_occupations, get_e_occu(0))
gs = Mpo.displacement(mol_list, 0, 2).apply(gs)
assert np.allclose(gs.e_occupations, get_e_occu(2))
gs = Mpo.displacement(mol_list, 2, 0).apply(gs)
assert np.allclose(gs.e_occupations ,get_e_occu(0))
