TimeDependentExpMPOEvolution¶
full name: tenpy.algorithms.mpo_evolution.TimeDependentExpMPOEvolution
parent module:
tenpy.algorithms.mpo_evolution
type: class
Inheritance Diagram

Methods
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Calculate |
Return necessary data to resume a |
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Re-initialize a new self.model at current self.evolved_time. |
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Resume a run that was interrupted. |
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Run the real-time evolution with the W_I/W_II approximation. |
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Initialize algorithm from another algorithm instance of a different class. |
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Time evolve by N_steps steps. |
Class Attributes and Properties
whether the algorithm supports time-dependent H |
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- class tenpy.algorithms.mpo_evolution.TimeDependentExpMPOEvolution(psi, model, options, **kwargs)[source]¶
Bases:
ExpMPOEvolution
Variant of
ExpMPOEvolution
that can handle time-dependent hamiltonians.As of now, it only supports first
ExpMPOEvolution.order
with a very basic implementation, that just reinitializes the model after each time evolution steps with an updated model parameter time set toevolved_time
. The model class should read that parameter.Todo
This is still under development and lacks rigorous tests.
- time_dependent_H = True¶
whether the algorithm supports time-dependent H
- update(N_steps)[source]¶
Time evolve by N_steps steps.
- Parameters
N_steps (int) – The number of time steps psi is evolved by.
- Returns
trunc_err – Truncation error induced during the update.
- Return type
- calc_U(dt, order, approximation)[source]¶
Calculate
self._U_MPO
.This function calculates the approximation
U ~= exp(-i dt_ H)
withdt_ = dt` for ``order=1
, ordt_ = (1 - 1j)/2 dt
anddt_ = (1 + 1j)/2 dt
fororder=2
.
- reinit_model()[source]¶
Re-initialize a new self.model at current self.evolved_time.
- Returns
New instance of the model initialized at
model_params['time'] = self.evolved_time
.- Return type
model
- get_resume_data(sequential_simulations=False)[source]¶
Return necessary data to resume a
run()
interrupted at a checkpoint.At a
checkpoint
, you can savepsi
,model
andoptions
along with the data returned by this function. When the simulation aborts, you can resume it using this saved data with:eng = AlgorithmClass(psi, model, options, resume_data=resume_data) eng.resume_run()
An algorithm which doesn’t support this should override resume_run to raise an Error.
- Parameters
sequential_simulations (bool) – If True, return only the data for re-initializing a sequential simulation run, where we “adiabatically” follow the evolution of a ground state (for variational algorithms), or do series of quenches (for time evolution algorithms); see
run_seq_simulations()
.- Returns
resume_data – Dictionary with necessary data (apart from copies of psi, model, options) that allows to continue the simulation from where we are now. It might contain an explicit copy of psi.
- Return type
- resume_run()[source]¶
Resume a run that was interrupted.
In case we saved an intermediate result at a
checkpoint
, this function allows to resume therun()
of the algorithm (after re-initialization with the resume_data). Since most algorithms just have a while loop with break conditions, the default behaviour implemented here is to just callrun()
.
- classmethod switch_engine(other_engine, *, options=None, **kwargs)[source]¶
Initialize algorithm from another algorithm instance of a different class.
You can initialize one engine from another, not too different subclasses. Internally, this function calls
get_resume_data()
to extract data from the other_engine and then initializes the new class.Note that it transfers the data without making copies in most case; even the options! Thus, when you call run() on one of the two algorithm instances, it will modify the state, environment, etc. in the other. We recommend to make the switch as
engine = OtherSubClass.switch_engine(engine)
directly replacing the reference.- Parameters
cls (class) – Subclass of
Algorithm
to be initialized.other_engine (
Algorithm
) – The engine from which data should be transfered. Another, but not too different algorithm subclass-class; e.g. you can switch from theTwoSiteDMRGEngine
to theOneSiteDMRGEngine
.options (None | dict-like) – If not None, these options are used for the new initialization. If None, take the options from the other_engine.
**kwargs – Further keyword arguments for class initialization. If not defined, resume_data is collected with
get_resume_data()
.