def test_ZeroTcorr_TDVP(method, evolve_dt, use_rk, rtol):
# procedure = [[50, 0], [50, 0], [50, 0]]
procedure = [[20, 0], [20, 0], [20, 0]]
optimize_config = OptimizeConfig(procedure=procedure)
mol_list = parameter.mol_list
evolve_config = EvolveConfig(method, evolve_dt=evolve_dt, adaptive=False)
evolve_config.tdvp_ps_rk4 = use_rk
zero_t_corr = SpectraTwoWayPropZeroT(
mol_list,
"abs",
optimize_config,
evolve_config=evolve_config,
offset=Quantity(2.28614053, "ev"),
)
zero_t_corr.info_interval = 30
nsteps = 200
# nsteps = 1200
zero_t_corr.evolve(evolve_dt, nsteps)
with open(
os.path.join(
cur_dir,
"zero_t_%s.npy" % str(evolve_config.method).split(".")[1]),
"rb",
) as f:
ZeroTabs_std = np.load(f)
assert np.allclose(zero_t_corr.autocorr[:nsteps],
ZeroTabs_std[:nsteps],
rtol=rtol)
def test_autocorr(scheme, mollist2):
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 2)
mol = Mol(Quantity(0), [ph])
mol_list1 = MolList([mol] * 5, Quantity(1), scheme)
if mollist2:
mol_list = MolList2.MolList_to_MolList2(mol_list1)
else:
mol_list = mol_list1
temperature = Quantity(50000, 'K')
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=24)
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=0.5,
adaptive_rtol=1e-3)
ievolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=-0.1j)
ac = TransportAutoCorr(mol_list,
temperature,
compress_config=compress_config,
ievolve_config=ievolve_config,
evolve_config=evolve_config)
ac.evolve(nsteps=5, evolve_time=5)
corr_real = ac.auto_corr.real
exact_real = get_exact_autocorr(mol_list1, temperature,
ac.evolve_times_array).real
atol = 1e-2
# direct comparison may fail because of different sign
assert np.allclose(corr_real, exact_real, atol=atol) or np.allclose(
corr_real, -exact_real, atol=atol)
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__(self):
super(Mps, self).__init__()
# todo: tdh part with GPU backend
self.wfns = [1]
self.optimize_config: OptimizeConfig = OptimizeConfig()
self.evolve_config: EvolveConfig = EvolveConfig()
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(max_bonddim=12)
mpdm.evolve_config = EvolveConfig(EvolveMethod.tdvp_ps)
offset = mpdm.expectation(tentative_mpo)
mpo = Mpo(mol_list, offset=Quantity(offset, "a.u."))
mpdm = mpdm.expand_bond_dimension(mpo)
# do the evolution
# nsteps = 90 # too many steps, may take hours to finish
nsteps = 20
dt = 20.0
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[:, :2*nsteps:2], atol=1e-3, rtol=1e-3)
def __init__(self,
mol_list,
spectratype,
temperature,
freq_reg,
m_max,
eta,
icompress_config=None,
ievolve_config=None,
insteps=None,
method='1site',
procedure_cv=None,
dump_dir: str = None,
job_name=None,
cores=1):
super().__init__(mol_list, spectratype, freq_reg, m_max, eta, method,
procedure_cv, cores)
self.temperature = temperature
self.evolve_config = ievolve_config
self.compress_config = icompress_config
if self.evolve_config is None:
self.evolve_config = \
EvolveConfig()
if self.compress_config is None:
self.compress_config = \
CompressConfig(CompressCriteria.fixed,
max_bonddim=m_max)
self.compress_config.set_bonddim(len(mol_list.pbond_list))
self.insteps = insteps
self.job_name = job_name
self.dump_dir = dump_dir
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 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,
spectratype,
temperature,
insteps,
offset,
evolve_config=None,
icompress_config=None,
ievolve_config=None,
gs_shift=0,
dump_dir: str = None,
job_name=None,
):
self.temperature = temperature
self.insteps = insteps
self.gs_shift = gs_shift
self.icompress_config = icompress_config
self.ievolve_config = ievolve_config
if self.icompress_config is None:
self.icompress_config = CompressConfig()
if self.ievolve_config is None:
self.ievolve_config = EvolveConfig()
self.dump_dir = dump_dir
self.job_name = job_name
super(SpectraFiniteT, self).__init__(mol_list,
spectratype,
temperature,
evolve_config=evolve_config,
offset=offset,
dump_dir=dump_dir,
job_name=job_name)
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__(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 test_thermal_equilibrium(periodic):
if periodic:
# define properties
# periodic case
prop_mpos = ops.e_ph_static_correlation(mol_list, periodic=True)
prop_strs = list(prop_mpos.keys())
prop_strs.append("e_rdm")
prop = Property(prop_strs, prop_mpos)
else:
# non-periodic case (though actually periodic)
prop_mpos = {}
for imol in range(nmols):
prop_mpo = ops.e_ph_static_correlation(mol_list, imol=imol)
prop_mpos.update(prop_mpo)
prop_strs = list(prop_mpos.keys())
prop_strs.append("e_rdm")
prop = Property(prop_strs, prop_mpos)
beta = Quantity(1500., "K").to_beta()
logger.info(f"beta:{beta}")
nsteps = 1
dbeta = beta / nsteps / 2j
evolve_config = EvolveConfig(method=EvolveMethod.prop_and_compress, adaptive=True,
adaptive_rtol=1e-4, guess_dt=0.1/1j)
init_mpdm = MpDm.max_entangled_ex(mol_list)
#init_mpdm.compress_config.bond_dim_max_value=10
init_mpdm.compress_config.threshold = 1e-4
td = ThermalProp(init_mpdm, mpo, evolve_config=evolve_config,
dump_dir=dump_dir, job_name=job_name, properties=prop)
td.evolve(dbeta, nsteps=nsteps)
if periodic:
def combine(local_prop):
res = []
for dis in range(nmols):
res.append(local_prop.prop_res["S_"+str(dis)+"_0"][-1])
return res
else:
def combine(local_prop):
e_ph_static_corr = []
for dis in range(nmols):
res = 0.
for i in range(nmols):
res = res + np.array(local_prop.prop_res["S_"+str(i)+"_"+str((i+dis)%nmols)+"_0"][-1])
e_ph_static_corr.append(res)
return e_ph_static_corr
assert np.allclose(td.properties.prop_res["e_rdm"][-1], thermal_std["e_rdm"])
assert np.allclose(combine(td.properties), thermal_std["e_ph_static_corr"])
# directly calculate properties
mpdm = td.latest_mps
prop.calc_properties(mpdm, None)
assert np.allclose(prop.prop_res["e_rdm"][-1], prop.prop_res["e_rdm"][-2])
def test_adaptive_zero_t(method):
np.random.seed(0)
evolve_config = EvolveConfig(method=method, guess_dt=0.1, adaptive=True)
ct = ChargeTransport(band_limit_mol_list,
evolve_config=evolve_config,
stop_at_edge=True)
ct.evolve(evolve_dt=5.)
assert_band_limit(ct, 1e-2)
def test_bandlimit_zero_t(method, evolve_dt, nsteps, rtol, scheme):
np.random.seed(0)
evolve_config = EvolveConfig(method)
ct = ChargeTransport(band_limit_mol_list.switch_scheme(scheme),
evolve_config=evolve_config)
ct.stop_at_edge = True
ct.evolve(evolve_dt, nsteps)
assert_band_limit(ct, rtol)
def test_dump(method, dt):
mps = init_mps.copy()
mps.evolve_config = EvolveConfig(method)
# dump all matrices
mps.compress_config = CompressConfig(CompressCriteria.fixed, dump_matrix_size=1)
evolved_mps = check_result(mps, mpo, dt, 5)
# check all matrices are actually dumped
for mt in evolved_mps._mp:
assert isinstance(mt, str)
def test_tdvp_vmf(init_state, with_mu, force_ovlp, atol):
mps = init_state.copy()
method = EvolveMethod.tdvp_mu_vmf if with_mu else EvolveMethod.tdvp_vmf
mps.evolve_config = EvolveConfig(method,
ivp_rtol=1e-4,
ivp_atol=1e-7,
force_ovlp=force_ovlp)
mps.evolve_config.vmf_auto_switch = False
check_result(mps, mpo, 0.5, 2, atol)
def test_thermal_prop(mol_list, etot_std, occ_std, nsteps, evolve_method,
use_rk, rtol):
init_mps = MpDm.max_entangled_ex(mol_list)
mpo = Mpo(mol_list)
beta = Quantity(298, "K").to_beta()
evolve_time = beta / 2j
if nsteps is None:
evolve_config = EvolveConfig(evolve_method,
adaptive=True,
evolve_dt=beta / 100j)
else:
evolve_config = EvolveConfig(evolve_method)
if evolve_method is EvolveMethod.tdvp_ps:
evolve_config.tdvp_ps_rk4 = use_rk
tp = ThermalProp(init_mps, mpo, evolve_config=evolve_config)
tp.evolve(nsteps=nsteps, evolve_time=evolve_time)
mps = tp.latest_mps
MPO, HAM, Etot, A_el = mps.construct_hybrid_Ham(mpo, debug=True)
assert np.allclose(Etot, etot_std, rtol=rtol)
assert np.allclose(A_el, occ_std, rtol=rtol)
def __init__(
self,
model,
spectratype,
m_max,
eta,
temperature,
h_mpo=None,
method='1site',
procedure_cv=None,
rtol=1e-5,
b_mps=None,
cv_mps=None,
icompress_config=None,
ievolve_config=None,
insteps=None,
dump_dir: str = None,
job_name=None,
):
self.temperature = temperature
self.evolve_config = ievolve_config
self.compress_config = icompress_config
if self.evolve_config is None:
self.evolve_config = \
EvolveConfig()
if self.compress_config is None:
self.compress_config = \
CompressConfig(CompressCriteria.fixed,
max_bonddim=m_max)
self.compress_config.set_bonddim(len(model.pbond_list))
self.insteps = insteps
self.job_name = job_name
self.dump_dir = dump_dir
super().__init__(
model,
spectratype,
m_max,
eta,
h_mpo=h_mpo,
method=method,
procedure_cv=procedure_cv,
rtol=rtol,
b_mps=b_mps,
cv_mps=cv_mps,
)
self.cv_mpo = self.cv_mps
self.b_mpo = self.b_mps
self.a_oper = None
def test_sbm_zt(alpha):
raw_delta = Quantity(1)
raw_omega_c = Quantity(20)
n_phonons = 3
mol_list = param2mollist(alpha, raw_delta, raw_omega_c, 5, n_phonons)
evolve_config = EvolveConfig(method=EvolveMethod.tdvp_ps, adaptive=True, guess_dt=0.1)
sbm = SpinBosonModel(mol_list, Quantity(0), evolve_config=evolve_config)
sbm.evolve(nsteps=20, evolve_time=20)
spin1 = sbm.sigma_z
spin2 = get_exact_zt(mol_list[0], sbm.evolve_times)
assert np.allclose(spin1, spin2, atol=1e-3)
def test_ft():
model = get_model()
mpo = Mpo(model)
impdm = MpDm.max_entangled_gs(model)
impdm.compress_config = CompressConfig(threshold=1e-6)
temperature = Quantity(3)
evolve_config = EvolveConfig(adaptive=True, guess_dt=-0.001j)
tp = ThermalProp(impdm, mpo, evolve_config=evolve_config)
tp.evolve(nsteps=1, evolve_time=temperature.to_beta() / 2j)
mpdm = tp.latest_mps
mpdm = Mpo(model, Op("sigma_x", "spin")).contract(mpdm)
mpdm.evolve_config = EvolveConfig(adaptive=True, guess_dt=0.1)
time_series = [0]
sigma_z_oper = Mpo(model, Op("sigma_z", "spin"))
spin = [mpdm.expectation(sigma_z_oper)]
for i in range(29):
dt = mpdm.evolve_config.guess_dt
mpdm = mpdm.evolve(mpo, evolve_dt=dt)
time_series.append(time_series[-1] + dt)
spin.append(mpdm.expectation(sigma_z_oper))
qutip_res = get_qutip_ft(model, temperature, time_series)
assert np.allclose(qutip_res, spin, atol=1e-3)
def test_ft():
mol = get_mol()
mol_list = MolList([mol], Quantity(0))
mpo = Mpo(mol_list)
impdm = MpDm.max_entangled_gs(mol_list)
impdm.compress_config = CompressConfig(threshold=1e-6)
impdm.use_dummy_qn = True
temperature = Quantity(3)
evolve_config = EvolveConfig(adaptive=True, guess_dt=-0.001j)
tp = ThermalProp(impdm, mpo, evolve_config=evolve_config)
tp.evolve(nsteps=1, evolve_time=temperature.to_beta() / 2j)
mpdm = tp.latest_mps
mpdm = Mpo.onsite(mol_list, r"sigma_x").contract(mpdm)
mpdm.evolve_config = EvolveConfig(adaptive=True, guess_dt=0.1)
time_series = [0]
spin = [1 - 2 * mpdm.e_occupations[0]]
for i in range(30):
dt = mpdm.evolve_config.guess_dt
mpdm = mpdm.evolve(mpo, evolve_dt=dt)
time_series.append(time_series[-1] + dt)
spin.append(1 - 2 * mpdm.e_occupations[0])
exact = get_exact_ft(mol, temperature, time_series)
assert np.allclose(exact, spin, atol=1e-3)
def test_holstein_kubo(scheme):
ph = Phonon.simple_phonon(Quantity(1), Quantity(1), 2)
mol = Mol(Quantity(0), [ph])
model = HolsteinModel([mol] * 5, Quantity(1), scheme)
temperature = Quantity(50000, 'K')
compress_config = CompressConfig(CompressCriteria.fixed, max_bonddim=24)
evolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=0.5,
adaptive_rtol=1e-3)
ievolve_config = EvolveConfig(EvolveMethod.tdvp_ps,
adaptive=True,
guess_dt=-0.1j)
kubo = TransportKubo(model,
temperature,
compress_config=compress_config,
ievolve_config=ievolve_config,
evolve_config=evolve_config)
kubo.evolve(nsteps=5, evolve_time=5)
qutip_res = get_qutip_holstein_kubo(model, temperature,
kubo.evolve_times_array)
rtol = 5e-2
assert np.allclose(kubo.auto_corr, qutip_res, rtol=rtol)
def test_gaussian_bond_dim():
compress_config = CompressConfig(
criteria=CompressCriteria.fixed,
bonddim_distri=BondDimDistri.center_gauss,
max_bonddim=10,
)
evolve_config = EvolveConfig(guess_dt=0.1, adaptive=True)
ct = ChargeTransport(
band_limit_mol_list,
compress_config=compress_config,
evolve_config=evolve_config,
)
ct.stop_at_edge = True
ct.evolve(evolve_dt=2.)
assert_band_limit(ct, 1e-2)
def test_finite_t_spectra_emi_TDVP(method, nsteps, evolve_dt, use_rk, rtol,
interval):
mol_list = parameter.mol_list
temperature = Quantity(298, "K")
offset = Quantity(2.28614053, "ev")
evolve_config = EvolveConfig(method)
evolve_config.tdvp_ps_rk4 = use_rk
finite_t_corr = SpectraFiniteT(mol_list,
"emi",
temperature,
50,
offset,
evolve_config=evolve_config)
finite_t_corr.evolve(evolve_dt, nsteps)
with open(
os.path.join(
cur_dir,
"finite_t_%s.npy" % str(evolve_config.method).split(".")[1]),
"rb",
) as fin:
std = np.load(fin)
assert np.allclose(finite_t_corr.autocorr[:nsteps],
std[:interval * nsteps:interval],
rtol=rtol)
def test_ft_emi(model):
with open(os.path.join(cur_dir, "emi_ft.npy"), "rb") as fin:
standard_value = np.load(fin)
# the standard value is plotted over np.arange(-0.11, -0.05, 5.e-4)
freq_reg = np.arange(-0.11, -0.05, 5.e-4).tolist()
test_freq = [freq_reg[52]]
T = Quantity(298, unit='K')
standard_value = [standard_value[52]]
evolve_config = EvolveConfig(method=EvolveMethod.tdvp_ps)
compress_config = CompressConfig(criteria=CompressCriteria.fixed, max_bonddim=10)
spectra = SpectraFtCV(model, "emi", 10,
5.e-3, T, ievolve_config=evolve_config,
icompress_config=compress_config,
insteps=10, rtol=1e-3)
result = batch_run(test_freq, 1, spectra)
assert np.allclose(result, standard_value, rtol=1.e-2)
def compare():
dt_list = [0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
method_list = [EvolveMethod.prop_and_compress, EvolveMethod.tdvp_mu_vmf, EvolveMethod.tdvp_mu_cmf, EvolveMethod.tdvp_ps]
all_values = []
for method in method_list:
values = []
for dt in dt_list:
mps = init_mps.copy()
mps.compress_config = CompressConfig(CompressCriteria.fixed)
mps.evolve_config = EvolveConfig(method)
mps.evolve_config._adjust_bond_dim_counter = True
mps.evolve_config.force_ovlp = True
values.append(check_result(mps, mpo, dt, 2))
print(values)
all_values.append(values)
print(mps.bond_dims)
print(all_values)
def test_sbm_zt(alpha):
raw_delta = Quantity(1)
raw_omega_c = Quantity(20)
n_phonons = 3
mol_list = param2mollist(alpha, raw_delta, raw_omega_c, 5, n_phonons)
compress_config = CompressConfig(threshold=1e-4)
evolve_config = EvolveConfig(adaptive=True, evolve_dt=0.1)
sbm = SpinBosonModel(mol_list,
Quantity(0),
compress_config=compress_config,
evolve_config=evolve_config)
sbm.evolve(evolve_time=20)
spin1 = sbm.sigma_z
spin2 = get_exact_zt(mol_list[0], sbm.evolve_times)
assert np.allclose(spin1, spin2, atol=1e-3)
def __init__(
self,
mol_list,
temperature: Quantity,
j_oper: Mpo = 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.mol_list = mol_list
self.h_mpo = Mpo(mol_list)
logger.info(f"Bond dim of h_mpo: {self.h_mpo.bond_dims}")
if j_oper is None:
self.j_oper = self._construct_flux_operator()
else:
self.j_oper = j_oper
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 = []
super().__init__(evolve_config=evolve_config,
dump_dir=dump_dir,
job_name=job_name)
def test_zt():
mol = get_mol()
mol_list = MolList([mol], Quantity(0))
mps = Mps.gs(mol_list, False)
mps.compress_config = CompressConfig(threshold=1e-6)
mps.evolve_config = EvolveConfig(adaptive=True, guess_dt=0.1)
mps.use_dummy_qn = True
mpo = Mpo(mol_list)
time_series = [0]
spin = [1]
for i in range(30):
dt = mps.evolve_config.guess_dt
mps = mps.evolve(mpo, evolve_dt=dt)
time_series.append(time_series[-1] + dt)
spin.append(1 - 2 * mps.e_occupations[0])
exact = get_exact_zt(mol, time_series)
assert np.allclose(exact, spin, atol=1e-3)
def test_zt():
model = get_model()
mps = Mps.ground_state(model, False)
mps.compress_config = CompressConfig(threshold=1e-6)
mps.evolve_config = EvolveConfig(adaptive=True, guess_dt=0.1)
mps.use_dummy_qn = True
mpo = Mpo(model)
time_series = [0]
spin = [1]
sigma_z_oper = Mpo(model, Op("sigma_z", "spin"))
for i in range(30):
dt = mps.evolve_config.guess_dt
mps = mps.evolve(mpo, evolve_dt=dt)
time_series.append(time_series[-1] + dt)
spin.append(mps.expectation(sigma_z_oper))
qutip_res = get_qutip_zt(model, time_series)
assert np.allclose(qutip_res, spin, atol=1e-3)
