TimeDependentExpMPOEvolution

Inheritance Diagram

Inheritance diagram of tenpy.algorithms.mpo_evolution.TimeDependentExpMPOEvolution

Methods

TimeDependentExpMPOEvolution.__init__(psi, ...)

TimeDependentExpMPOEvolution.calc_U(dt, ...)

Calculate self._U_MPO.

TimeDependentExpMPOEvolution.get_resume_data([...])

Return necessary data to resume a run() interrupted at a checkpoint.

TimeDependentExpMPOEvolution.reinit_model()

Re-initialize a new self.model at current self.evolved_time.

TimeDependentExpMPOEvolution.resume_run()

Resume a run that was interrupted.

TimeDependentExpMPOEvolution.run()

Run the real-time evolution with the W_I/W_II approximation.

TimeDependentExpMPOEvolution.switch_engine(...)

Initialize algorithm from another algorithm instance of a different class.

TimeDependentExpMPOEvolution.update(N_steps)

Time evolve by N_steps steps.

Class Attributes and Properties

TimeDependentExpMPOEvolution.time_dependent_H

whether the algorithm supports time-dependent H

TimeDependentExpMPOEvolution.verbose

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 to evolved_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

run()[source]

Run the real-time evolution with the W_I/W_II approximation.

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

TruncationError

calc_U(dt, order, approximation)[source]

Calculate self._U_MPO.

This function calculates the approximation U ~= exp(-i dt_ H) with dt_ = dt` for ``order=1, or dt_ = (1 - 1j)/2 dt and dt_ = (1 + 1j)/2 dt for order=2.

Parameters
  • dt (float) – Size of the time-step used in calculating _U

  • order (int) – The order of the algorithm. Only 1 and 2 are allowed.

  • approximation ('I' or 'II') – Type of approximation for the time evolution operator.

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 save psi, model and options 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

dict

resume_run()[source]

Resume a run that was interrupted.

In case we saved an intermediate result at a checkpoint, this function allows to resume the run() 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 call run().

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 the TwoSiteDMRGEngine to the OneSiteDMRGEngine.

  • 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().