TimeDependentExpMPOEvolution

Inheritance Diagram

Inheritance diagram of tenpy.algorithms.mpo_evolution.TimeDependentExpMPOEvolution

Methods

TimeDependentExpMPOEvolution.__init__(psi, ...)

TimeDependentExpMPOEvolution.calc_U(dt[, ...])

Calculate self._U_MPO.

TimeDependentExpMPOEvolution.estimate_RAM([...])

Gives an approximate prediction for the required memory usage.

TimeDependentExpMPOEvolution.evolve(N_steps, dt)

Evolve by N_steps*dt.

TimeDependentExpMPOEvolution.evolve_step(dt)

TimeDependentExpMPOEvolution.get_resume_data([...])

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

TimeDependentExpMPOEvolution.prepare_evolve(dt)

Prepare an evolution step.

TimeDependentExpMPOEvolution.reinit_model()

Re-initialize a new model at current evolved_time.

TimeDependentExpMPOEvolution.resume_run()

Resume a run that was interrupted.

TimeDependentExpMPOEvolution.run()

Perform a (real-)time evolution of psi by N_steps * dt.

TimeDependentExpMPOEvolution.run_evolution(...)

Run the time evolution for N_steps * dt.

TimeDependentExpMPOEvolution.switch_engine(...)

Initialize algorithm from another algorithm instance of a different class.

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: TimeDependentHAlgorithm, ExpMPOEvolution

Variant of ExpMPOEvolution that can handle time-dependent hamiltonians.

See details in TimeDependentHAlgorithm as well.

calc_U(dt, order=2, approximation='II')[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.

estimate_RAM(mem_saving_factor=None)[source]

Gives an approximate prediction for the required memory usage.

This calculation is based on the requested bond dimension, the local Hilbert space dimension, the number of sites, and the boundary conditions.

Parameters:

mem_saving_factor (float) – Represents the amount of RAM saved due to conservation laws. By default, it is ‘None’ and is extracted from the model automatically. However, this is only possible in a few cases and needs to be estimated in most cases. This is due to the fact that it is dependent on the model parameters. If one has a better estimate, one can pass the value directly. This value can be extracted by building the initial state psi (usually by performing DMRG) and then calling print(psi.get_B(0).sparse_stats()) TeNPy will automatically print the fraction of nonzero entries in the first line, for example, 6 of 16 entries (=0.375) nonzero. This fraction corresponds to the mem_saving_factor; in our example, it is 0.375.

Returns:

usage – Required RAM in MB.

Return type:

float

See also

tenpy.simulations.simulation.estimate_simulation_RAM

global function calling this.

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

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

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.

reinit_model()[source]

Re-initialize a new model at current evolved_time.

Skips re-initialization if the model.options['time'] is the same as evolved_time. The model should read out the option 'time' and initialize the corresponding H(t).

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 behavior implemented here is to just call run().

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]

Run the time evolution for N_steps * dt.

Updates the model after each time step dt to account for changing H(t). For parameters see TimeEvolutionAlgorithm.

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 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().

time_dependent_H = True

whether the algorithm supports time-dependent H