def test_zero_t_abs(algorithm, compress_method, ph_info, rtol, switch_to_64backend):
np.random.seed(0)
# print "data", value
procedure = [[1, 0], [1, 0], [1, 0]]
optimize_config = OptimizeConfig()
optimize_config.procedure = procedure
mol_list = parameter.custom_mol_list(None, *ph_info)
if algorithm == 1:
SpectraZeroT = SpectraOneWayPropZeroT
else:
SpectraZeroT = SpectraTwoWayPropZeroT
zero_t_corr = SpectraZeroT(
mol_list.switch_scheme(2), "abs", optimize_config, offset=parameter.offset
)
zero_t_corr.info_interval = 30
nsteps = 100
dt = 30.0
zero_t_corr.evolve(dt, nsteps)
with open(
os.path.join(
cur_dir, "ZeroTabs_" + str(algorithm) + str(compress_method) + ".npy"
),
"rb",
) as f:
std = np.load(f)
assert np.allclose(zero_t_corr.autocorr[:nsteps], std[:nsteps], rtol=rtol)
def test_zero_t_abs_mposcheme3(
algorithm, compress_method, ph_info, rtol, switch_to_64backend
):
np.random.seed(0)
# print "data", value
j_matrix = (
np.array([[0.0, -0.1, 0.0], [-0.1, 0.0, -0.3], [0.0, -0.3, 0.0]])
/ constant.au2ev
)
procedure = [[1, 0], [1, 0], [1, 0]]
mol_list = parameter.custom_mol_list(j_matrix, *ph_info)
nsteps = 50
dt = 30.0
if algorithm == 1:
SpectraZeroT = SpectraOneWayPropZeroT
else:
SpectraZeroT = SpectraTwoWayPropZeroT
optimize_config = OptimizeConfig()
optimize_config.procedure = procedure
zero_t_corr2 = SpectraZeroT(
mol_list, "abs", optimize_config, offset=parameter.offset
)
zero_t_corr2.evolve(dt, nsteps)
zero_t_corr3 = SpectraZeroT(
mol_list.switch_scheme(3), "abs", optimize_config, offset=parameter.offset
)
zero_t_corr3.evolve(dt, nsteps)
assert np.allclose(zero_t_corr2.autocorr, zero_t_corr3.autocorr, rtol=rtol)
def test_zero_t_abs(algorithm):
np.random.seed(0)
# print "data", value
procedure = [[1, 0], [1, 0], [1, 0]]
optimize_config = OptimizeConfig()
optimize_config.procedure = procedure
model = parameter.holstein_model
if algorithm == 1:
SpectraZeroT = SpectraOneWayPropZeroT
else:
SpectraZeroT = SpectraTwoWayPropZeroT
zero_t_corr = SpectraZeroT(model.switch_scheme(2),
"abs",
optimize_config,
offset=parameter.offset)
zero_t_corr.info_interval = 30
nsteps = 100
dt = 30.0
zero_t_corr.evolve(dt, nsteps)
with open(
os.path.join(cur_dir, "ZeroTabs_" + str(algorithm) + "svd.npy"),
"rb",
) as f:
std = np.load(f)
assert np.allclose(zero_t_corr.autocorr[:nsteps], std[:nsteps], rtol=1e-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 __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_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 __init__(
self,
model,
spectratype,
temperature=Quantity(0, "K"),
optimize_config=None,
offset=Quantity(0),
ex_shift=0,
gs_shift=0,
):
# != 0 cases not tested
assert ex_shift == gs_shift == 0
assert temperature == 0
if spectratype == "emi":
self.space1 = "EX"
self.space2 = "GS"
self.shift1 = ex_shift
self.shift2 = gs_shift
if temperature != 0:
assert len(model) == 1
else:
assert len(model) == 1
self.space1 = "GS"
self.space2 = "EX"
self.shift1 = gs_shift
self.shift2 = ex_shift
if optimize_config is None:
optimize_config = OptimizeConfig()
self.optimize_config = optimize_config
super(SpectraExact, self).__init__(model,
spectratype,
temperature,
offset=offset)
self.i_mps = self.latest_mps.ket_mps
self.e_mean = self.i_mps.expectation(self.h_mpo)
def __init__(
self,
model,
spectratype,
optimize_config=None,
evolve_config=None,
offset=Quantity(0),
):
if optimize_config is None:
self.optimize_config = OptimizeConfig()
else:
self.optimize_config = optimize_config
super(SpectraZeroT, self).__init__(model, spectratype, Quantity(0),
evolve_config, offset)
def init_oper(self):
if self.spectratype == "abs":
self.nexciton = 0
dipoletype = r"a^\dagger"
else:
self.nexciton = 1
dipoletype = "a"
dipole_mpo = \
Mpo.onsite(
self.mol_list, dipoletype, dipole=True
)
mps, self.mpo = \
construct_mps_mpo_2(
self.mol_list, self.procedure_gs[0][0], self.nexciton
)
# ground state calculation
mps.optimize_config = OptimizeConfig(procedure=self.procedure_gs)
mps.optimize_config.method = "2site"
self.lowest_e = optimize_mps(mps, self.mpo)
ket_mps = dipole_mpo.apply(mps, canonicalise=True)
self.b_oper = ket_mps.scale(-self.eta)