def __init__(self, psi, model, TDVP_params, environment=None):
self.verbose = get_parameter(TDVP_params, 'verbose', 1, 'TDVP')
self.TDVP_params = TDVP_params
if environment is None:
environment = MPOEnvironment(psi, model.H_MPO, psi)
self.evolved_time = get_parameter(TDVP_params, 'start_time', 0., 'TDVP')
self.H_MPO = model.H_MPO
self.environment = environment
if not psi.finite:
raise ValueError("TDVP is only implemented for finite boundary conditions")
self.psi = psi
self.L = self.psi.L
self.dt = get_parameter(TDVP_params, 'dt', 2, 'TDVP')
self.trunc_params = get_parameter(TDVP_params, 'trunc_params', {}, 'TDVP')
self.N_steps = get_parameter(TDVP_params, 'N_steps', 10, 'TDVP')
def __init__(self, psi, model, options, environment=None, **kwargs):
TimeEvolutionAlgorithm.__init__(self, psi, model, options, **kwargs)
options = self.options
if model.H_MPO.explicit_plus_hc:
raise NotImplementedError(
"TDVP does not respect 'MPO.explicit_plus_hc' flag")
self.lanczos_options = options.subconfig('lanczos_options')
if environment is None:
environment = MPOEnvironment(psi, model.H_MPO, psi)
self.evolved_time = options.get('start_time', 0.)
self.H_MPO = model.H_MPO
self.environment = environment
if not psi.finite:
raise ValueError(
"TDVP is only implemented for finite boundary conditions")
self.L = self.psi.L
self.dt = options.get('dt', 2)
self.N_steps = options.get('N_steps', 10)
def __init__(self, psi, model, options, environment=None):
if model.H_MPO.explicit_plus_hc:
raise NotImplementedError("TDVP does not respect 'MPO.explicit_plus_hc' flag")
self.options = options = asConfig(options, "TDVP")
self.verbose = options.get('verbose', 1)
self.lanczos_options = options.subconfig('lanczos_options')
if environment is None:
environment = MPOEnvironment(psi, model.H_MPO, psi)
self.evolved_time = options.get('start_time', 0.)
self.H_MPO = model.H_MPO
self.environment = environment
if not psi.finite:
raise ValueError("TDVP is only implemented for finite boundary conditions")
self.psi = psi
self.L = self.psi.L
self.dt = options.get('dt', 2)
self.trunc_params = options.get('trunc_params', {})
self.N_steps = options.get('N_steps', 10)
[None, None, None, None, 0.5 * Jxx * Sm],
[None, None, None, None, 0.5 * Jxx * Sp],
[None, None, None, None, Jz * Sz ],
[None, None, None, None, Id ]] # yapf:disable
W = npc.grid_outer(W_grid, [mpo_leg, mpo_leg.conj()])
W.iset_leg_labels(['wL', 'wR', 'p', 'p*']) # wL/wR = virtual left/right of the MPO
Ws = [W] * L
Ws[0] = W[:1, :]
Ws[-1] = W[:, -1:]
H = MPO(psi.sites, Ws, psi.bc, IdL=0, IdR=-1)
print("3) define 'environments' left and right")
# this is automatically done during initialization of MPOEnvironment
env = MPOEnvironment(psi, H, psi)
envL = env.get_LP(0)
envR = env.get_RP(L - 1)
print("4) contract MPS and MPO to calculate the energy <psi|H|psi>")
E = env.full_contraction(L - 1)
print("E =", E)
print("5) calculate two-site hamiltonian ``H2`` from the MPO")
# label left, right physical legs with p, q
W0 = H.get_W(0).replace_labels(['p', 'p*'], ['p0', 'p0*'])
W1 = H.get_W(1).replace_labels(['p', 'p*'], ['p1', 'p1*'])
H2 = npc.tensordot(W0, W1, axes=('wR', 'wL')).itranspose(['wL', 'wR', 'p0', 'p1', 'p0*', 'p1*'])
H2 = H2[H.IdL[0], H.IdR[2]] # (If H has single-site terms, it's not that simple anymore)
print("H2 labels:", H2.get_leg_labels())