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_dynamics(dissipation, dt, nsteps):
tentative_mpo = Mpo(band_limit_mol_list)
gs_mp = MpDm.max_entangled_gs(band_limit_mol_list)
# subtract the energy otherwise might cause numeric error because of large offset * dbeta
energy = Quantity(gs_mp.expectation(tentative_mpo))
mpo = Mpo(band_limit_mol_list, offset=energy)
tp = ThermalProp(gs_mp, mpo, exact=True, space="GS")
tp.evolve(None, 50, low_t.to_beta() / 2j)
gs_mp = tp.latest_mps
center_mol_idx = band_limit_mol_list.mol_num // 2
creation_operator = Mpo.onsite(
band_limit_mol_list, r"a^\dagger", mol_idx_set={center_mol_idx}
)
mpdm = creation_operator.apply(gs_mp)
mpdm_full = MpDmFull.from_mpdm(mpdm)
# As more compression is involved higher threshold is necessary
mpdm_full.compress_config = CompressConfig(threshold=1e-4)
liouville = SuperLiouville(mpo, dissipation)
r_square_list = [calc_r_square(mpdm_full.e_occupations)]
time_series = [0]
for i in range(nsteps - 1):
logger.info(mpdm_full)
mpdm_full = mpdm_full.evolve(liouville, dt)
r_square_list.append(calc_r_square(mpdm_full.e_occupations))
time_series.append(time_series[-1] + dt)
time_series = np.array(time_series)
if dissipation == 0:
assert np.allclose(get_analytical_r_square(time_series), r_square_list, rtol=1e-2, atol=1e-3)
else:
# not much we can do, just basic sanity check
assert (np.array(r_square_list)[1:] < get_analytical_r_square(time_series)[1:]).all()
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 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_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 test_FT_dynamics_hybrid_TDDMRG_TDH(n_dmrg_phs, scheme):
mol_list = parameter_PBI.construct_mol(4, n_dmrg_phs, 10 - n_dmrg_phs).switch_scheme(scheme)
mpdm = MpDm.max_entangled_gs(mol_list)
tentative_mpo = Mpo(mol_list)
temperature = Quantity(2000, "K")
tp = ThermalProp(mpdm, tentative_mpo, exact=True, space="GS")
tp.evolve(None, 1, temperature.to_beta() / 2j)
mpdm = (
Mpo.onsite(mol_list, r"a^\dagger", mol_idx_set={0}).apply(tp.latest_mps).normalize(1.0)
)
mpdm.compress_config = CompressConfig(threshold=5e-4)
offset = mpdm.expectation(tentative_mpo)
mpo = Mpo(mol_list, offset=Quantity(offset, "a.u."))
# do the evolution
# nsteps = 90 # too many steps, may take hours to finish
nsteps = 40
dt = 10.0
occ = [mpdm.e_occupations]
for i in range(nsteps):
mpdm = mpdm.evolve(mpo, dt)
occ.append(mpdm.e_occupations)
# make it compatible with std data
occ = np.array(occ[:nsteps]).transpose()
with open(os.path.join(cur_dir, "FT_occ" + str(n_dmrg_phs) + ".npy"), "rb") as f:
std = np.load(f)
assert np.allclose(occ[:, :nsteps], std[:, :nsteps], atol=1e-3, rtol=1e-3)
def test_general_mpo_sbm():
mol = get_mol()
mol_list = MolList([mol], Quantity(0))
mol_list.mol_list2_para()
mpo = Mpo.general_mpo(mol_list,
const=Quantity(-mol_list[0].gs_zpe *
mol_list.mol_num))
mpo_std = Mpo(mol_list)
check_result(mpo, mpo_std)
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_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))
def test_general_mpo_MolList(mol_list, scheme):
if scheme == 4:
mol_list1 = mol_list.switch_scheme(4)
else:
mol_list1 = mol_list
mol_list1.mol_list2_para()
mpo = Mpo.general_mpo(mol_list1,
const=Quantity(-mol_list1[0].gs_zpe *
mol_list1.mol_num))
mpo_std = Mpo(mol_list1)
check_result(mpo, mpo_std)
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():
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 test_symbolic_mpo(nsites, nterms):
possible_operators = ["sigma_+", "sigma_-", "sigma_z"]
ham_terms = []
for i in range(nterms):
op_list = [
Op(random.choice(possible_operators), j) for j in range(nsites)
]
ham_terms.append(Op.product(op_list) * random.random())
basis = [BasisHalfSpin(i) for i in range(nsites)]
model = Model(basis, ham_terms)
mpo = Mpo(model)
dense_mpo = mpo.full_operator()
qutip_ham = get_spin_hamiltonian(ham_terms)
assert np.allclose(dense_mpo, qutip_ham.data.todense())
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 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_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__(self,
mol_list,
temperature: Quantity,
insteps: int = None,
ievolve_config=None,
compress_config=None,
evolve_config=None,
dump_dir: str = None,
job_name: str = None):
self.mol_list = mol_list
self.h_mpo = Mpo(mol_list)
self.j_oper = self._construct_flux_operator()
self.temperature = temperature
# imaginary time evolution config
if ievolve_config is None:
self.ievolve_config = EvolveConfig()
if insteps is None:
self.ievolve_config.adaptive = True
# start from a small step
self.ievolve_config.evolve_dt = temperature.to_beta() / 1e5j
self.ievolve_config.d_energy = 1
else:
self.ievolve_config = ievolve_config
self.insteps = insteps
if compress_config is None:
logger.debug("using default compress config")
self.compress_config = CompressConfig()
else:
self.compress_config = compress_config
self.impdm = None
super().__init__(evolve_config, dump_dir, job_name)
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 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 test_thermal_prop(adaptive, evolve_method):
model = parameter.holstein_model
init_mps = MpDm.max_entangled_ex(model)
mpo = Mpo(model)
beta = Quantity(298, "K").to_beta()
evolve_time = beta / 2j
evolve_config = EvolveConfig(evolve_method,
adaptive=adaptive,
guess_dt=0.1 / 1j)
if adaptive:
nsteps = 1
else:
nsteps = 100
if evolve_method == EvolveMethod.tdvp_mu_vmf:
nsteps = 20
evolve_config.ivp_rtol = 1e-3
evolve_config.ivp_atol = 1e-6
evolve_config.reg_epsilon = 1e-8
init_mps.compress_config.bond_dim_max_value = 12
dbeta = evolve_time / nsteps
tp = ThermalProp(init_mps, mpo, evolve_config=evolve_config)
tp.evolve(evolve_dt=dbeta, nsteps=nsteps)
# MPO, HAM, Etot, A_el = mps.construct_hybrid_Ham(mpo, debug=True)
# exact A_el: 0.20896541050347484, 0.35240029674394463, 0.4386342927525734
# exact internal energy: 0.0853388060014744
etot_std = 0.0853388 + parameter.holstein_model.gs_zpe
occ_std = [0.20896541050347484, 0.35240029674394463, 0.4386342927525734]
rtol = 5e-3
assert np.allclose(tp.e_occupations_array[-1], occ_std, rtol=rtol)
assert np.allclose(tp.energies[-1], etot_std, rtol=rtol)
def init_cv_mpo(self):
cv_mpo = Mpo.finiteT_cv(self.model,
1,
self.m_max,
self.spectratype,
percent=1.0)
return cv_mpo
def test_exact_propagator(dt, space, shift):
prop_mpo = Mpo.exact_propagator(holstein_model, -1.0j * dt, space, shift)
with open(os.path.join(cur_dir, "test_exact_propagator.pickle"),
"rb") as fin:
std_dict = pickle.load(fin)
std_mpo = std_dict[space]
assert prop_mpo == std_mpo
def __init__(
self,
model,
spectratype,
m_max,
eta,
h_mpo = None,
method = "1site",
procedure_cv = None,
rtol = 1e-5,
b_mps = None,
e0 = None,
cv_mps = None,
):
self.model = model
assert spectratype in ["abs", "emi", None]
self.spectratype = spectratype
self.m_max = m_max
self.eta = eta
# Hamiltonian
if h_mpo is None:
self.h_mpo = Mpo(model)
else:
self.h_mpo = h_mpo
assert method in ["1site", "2site"]
self.method = method
logger.info(f"cv optimize method: {method}")
# percent used to update correction vector for each isweep process
# see function mps.lib.select_basis
if procedure_cv is None:
procedure_cv = [0.4, 0.4, 0.2, 0.2, 0.1, 0.1] + [0] * 45
self.procedure_cv = procedure_cv
self.rtol = rtol
# ax=b b_mps and ground state energy e0
if b_mps is None:
self.b_mps, self.e0 = self.init_b_mps()
else:
self.b_mps = b_mps
# e0 is used in zero temperature case
self.e0 = e0
# initial_guess cv_mps
if cv_mps is None:
self.cv_mps = self.init_cv_mps()
else:
self.cv_mps = cv_mps
# results
self.hop_time = []
self.macro_iteration_result = []
self.batch_run = False
logger.info("DDMRG job created.")
def init_mps(self):
# first try to load
if self._defined_output_path:
mpdm = load_thermal_state(self.model, self._thermal_dump_path)
else:
mpdm = None
# then try to calculate
if mpdm is None:
i_mpdm = MpDm.max_entangled_ex(self.model)
i_mpdm.compress_config = self.compress_config
if self.job_name is None:
job_name = None
else:
job_name = self.job_name + "_thermal_prop"
tp = ThermalProp(i_mpdm, self.h_mpo, evolve_config=self.ievolve_config, dump_dir=self.dump_dir, job_name=job_name)
# only propagate half beta
tp.evolve(None, self.insteps, self.temperature.to_beta() / 2j)
mpdm = tp.latest_mps
if self._defined_output_path:
mpdm.dump(self._thermal_dump_path)
mpdm.compress_config = self.compress_config
e = mpdm.expectation(self.h_mpo)
self.h_mpo = Mpo(self.model, offset=Quantity(e))
mpdm.evolve_config = self.evolve_config
logger.debug("Applying current operator")
ket_mpdm = self.j_oper.contract(mpdm).canonical_normalize()
bra_mpdm = mpdm.copy()
if self.j_oper2 is None:
return BraKetPair(bra_mpdm, ket_mpdm, self.j_oper)
else:
logger.debug("Applying the second current operator")
ket_mpdm2 = self.j_oper2.contract(mpdm).canonical_normalize()
return BraKetPair(bra_mpdm, ket_mpdm, self.j_oper), BraKetPair(bra_mpdm, ket_mpdm2, self.j_oper2)
def __init__(self, model: Model, temperature: Quantity, distance_matrix: np.ndarray = None,
insteps: int=1, ievolve_config=None, compress_config=None,
evolve_config=None, dump_dir: str=None, job_name: str=None, properties: Property = None):
self.model = model
self.distance_matrix = distance_matrix
self.h_mpo = Mpo(model)
logger.info(f"Bond dim of h_mpo: {self.h_mpo.bond_dims}")
self._construct_current_operator()
if temperature == 0:
raise ValueError("Can't set temperature to 0.")
self.temperature = temperature
# imaginary time evolution config
if ievolve_config is None:
self.ievolve_config = EvolveConfig()
if insteps is None:
self.ievolve_config.adaptive = True
# start from a small step
self.ievolve_config.guess_dt = temperature.to_beta() / 1e5j
insteps = 1
else:
self.ievolve_config = ievolve_config
self.insteps = insteps
if compress_config is None:
logger.debug("using default compress config")
self.compress_config = CompressConfig()
else:
self.compress_config = compress_config
self.properties = properties
self._auto_corr = []
self._auto_corr_deomposition = []
super().__init__(evolve_config=evolve_config, dump_dir=dump_dir,
job_name=job_name)
def create_electron_fc(self, gs_mp):
center_mol_idx = self.mol_num // 2
creation_operator = Mpo.onsite(
self.mol_list, r"a^\dagger", mol_idx_set={center_mol_idx}
)
mps = creation_operator.apply(gs_mp)
return mps
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_mpo():
gs_dm = MpDm.max_entangled_gs(mol_list)
beta = Quantity(10, "K").to_beta()
tp = ThermalProp(gs_dm, Mpo(gs_dm.mol_list), exact=True, space="GS")
tp.evolve(None, 500, beta / 1j)
gs_dm = tp.latest_mps
mp = creation_operator.apply(gs_dm)
check_property(mp)
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 test_scheme4():
ph = Phonon.simple_phonon(Quantity(3.33), Quantity(1), 2)
m1 = Mol(Quantity(0), [ph])
m2 = Mol(Quantity(0), [ph]*2)
mlist1 = MolList([m1, m2], Quantity(17), 4)
mlist2 = MolList([m1, m2], Quantity(17), 3)
mpo4 = Mpo(mlist1)
assert mpo4.is_hermitian()
# for debugging
f = mpo4.full_operator()
mpo3 = Mpo(mlist2)
assert mpo3.is_hermitian()
# makeup two states
mps4 = Mps()
mps4.mol_list = mlist1
mps4.use_dummy_qn = True
mps4.append(np.array([1, 0]).reshape((1,2,1)))
mps4.append(np.array([0, 1]).reshape((1,2,1)))
mps4.append(np.array([0.707, 0.707]).reshape((1,2,1)))
mps4.append(np.array([1, 0]).reshape((1,2,1)))
mps4.build_empty_qn()
e4 = mps4.expectation(mpo4)
mps3 = Mps()
mps3.mol_list = mlist2
mps3.append(np.array([1, 0]).reshape((1,2,1)))
mps3.append(np.array([1, 0]).reshape((1,2,1)))
mps3.append(np.array([0, 1]).reshape((1,2,1)))
mps3.append(np.array([0.707, 0.707]).reshape((1,2,1)))
mps3.append(np.array([1, 0]).reshape((1,2,1)))
e3 = mps3.expectation(mpo3)
assert pytest.approx(e4) == e3
