TimeEvolutionAlgorithm¶

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

Inheritance Diagram

Methods

`TimeEvolutionAlgorithm.__init__`(psi, model, ...)
`TimeEvolutionAlgorithm.evolve`(N_steps, dt)	Evolve by N_steps*dt.
`TimeEvolutionAlgorithm.evolve_step`(dt)
`TimeEvolutionAlgorithm.get_resume_data`([...])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`TimeEvolutionAlgorithm.prepare_evolve`(dt)	Prepare an evolution step.
`TimeEvolutionAlgorithm.resume_run`()	Resume a run that was interrupted.
`TimeEvolutionAlgorithm.run`()	Perform a (real-)time evolution of `psi` by N_steps * dt.
`TimeEvolutionAlgorithm.run_evolution`(N_steps, dt)	Perform a (real-)time evolution of `psi` by N_steps * dt.
`TimeEvolutionAlgorithm.switch_engine`(...[, ...])	Initialize algorithm from another algorithm instance of a different class.

Class Attributes and Properties

`TimeEvolutionAlgorithm.time_dependent_H`	whether the algorithm supports time-dependent H
`TimeEvolutionAlgorithm.verbose`

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
dt	Minimal time step by which to evolve.
N_steps	Number of time steps `dt` to evolve by in :meth:`run`. [...]
preserve_norm	Whether the state will be normalized to its initial norm after each time st [...]
start_time	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 in run() 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 and True 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.

Type: float | complex

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 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

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 see TimeEvolutionAlgorithm.

prepare_evolve(dt)[source]¶

Prepare an evolution step.

This method is used to prepare repeated calls of evolve() given the model. For example, it may generate approximations of U=exp(-i H dt). To avoid overhead, it may cache the result depending on parameters/options; but it should always regenerate it if force_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

N_steps (int) – The number of time steps by dt to take at once.
dt (float) – Small time step. Might be ignored if already used in prepare_update().

Returns

trunc_err – Sum of truncation errors introduced during evolution.

Return type

TruncationError

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().