TimeEvolutionAlgorithm¶
full name: tenpy.algorithms.algorithm.TimeEvolutionAlgorithm
parent module:
tenpy.algorithms.algorithm
type: class
Inheritance Diagram

Methods
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Evolve by N_steps*dt. |
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Return necessary data to resume a |
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Prepare an evolution step. |
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Resume a run that was interrupted. |
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Perform a (real-)time evolution of |
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Perform a (real-)time evolution of |
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Initialize algorithm from another algorithm instance of a different class. |
Class Attributes and Properties
whether the algorithm supports time-dependent H |
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- class tenpy.algorithms.algorithm.TimeEvolutionAlgorithm(psi, model, options, **kwargs)[source]¶
Bases:
Algorithm
Common interface for (real) time evolution algorithms.
Parameters are the same as for
Algorithm
.Options
- config TimeEvolutionAlgorithm¶
option summary Minimal time step by which to evolve.
Number of time steps `dt` to evolve by in :meth:`run`. [...]
Whether the state will be normalized to its initial norm after each time st [...]
Initial value for :attr:`evolved_time`.
trunc_params (from Algorithm) in Algorithm
Truncation parameters as described in :cfg:config:`truncation`.
- option start_time: float¶
Initial value for
evolved_time
.
- option dt: float¶
Minimal time step by which to evolve.
- option N_steps: int¶
Number of time steps dt to evolve by in
run()
. Adjusting dt and N_steps at the same time allows to keep the evolution time done inrun()
fixed. Further, e.g., the Trotter decompositions of order > 1 are slightly more efficient if more than one step is performed at once.
- option preserve_norm: bool¶
Whether the state will be normalized to its initial norm after each time step. Per default, this is
False
for real time evolution andTrue
for imaginary time.
- evolved_time¶
Indicating how long psi has been evolved,
psi = exp(-i * evolved_time * H) psi(t=0)
. Not that the real-part of t is increasing for a real-time evolution, while the imaginary-part of t is decreasing for a imaginary time evolution.
- time_dependent_H = False¶
whether the algorithm supports time-dependent H
- 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
- run()[source]¶
Perform a (real-)time evolution of
psi
by N_steps * dt.You probably want to call this in a loop along with measurements. The recommended way to do this is via the
RealTimeEvolution
.
- run_evolution(N_steps, dt)[source]¶
Perform a (real-)time evolution of
psi
by N_steps * dt.This is the inner part of
run()
without the logging. For parameters seeTimeEvolutionAlgorithm
.
- prepare_evolve(dt)[source]¶
Prepare an evolution step.
This method is used to prepare repeated calls of
evolve()
given themodel
. For example, it may generate approximations ofU=exp(-i H dt)
. To avoid overhead, it may cache the result depending on parameters/options; but it should always regenerate it ifforce_prepare_evolve
is set.- Parameters
dt (float) – The time step to be used.
- evolve(N_steps, dt)[source]¶
Evolve by N_steps*dt.
Subclasses may override this with a more efficient way of do N_steps update_step.
- Parameters
- Returns
trunc_err – Sum of truncation errors introduced during evolution.
- 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 transferred. 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()
.