ExpMPOEvolution

full name: tenpy.algorithms.mpo_evolution.ExpMPOEvolution
parent module: tenpy.algorithms.mpo_evolution
type: class

Inheritance Diagram

Methods

`ExpMPOEvolution.__init__`(psi, model, ...)
`ExpMPOEvolution.calc_U`(dt[, order, ...])	Calculate `self._U_MPO`.
`ExpMPOEvolution.estimate_RAM`([mem_saving_factor])	Gives an approximate prediction for the required memory usage.
`ExpMPOEvolution.evolve`(N_steps, dt)	Evolve by N_steps*dt.
`ExpMPOEvolution.evolve_step`(dt)
`ExpMPOEvolution.get_resume_data`([...])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`ExpMPOEvolution.prepare_evolve`(dt)	Prepare an evolution step.
`ExpMPOEvolution.resume_run`()	Resume a run that was interrupted.
`ExpMPOEvolution.run`()	Perform a (real-)time evolution of `psi` by N_steps * dt.
`ExpMPOEvolution.run_evolution`(N_steps, dt)	Perform a (real-)time evolution of `psi` by N_steps * dt.
`ExpMPOEvolution.switch_engine`(other_engine, *)	Initialize algorithm from another algorithm instance of a different class.

Class Attributes and Properties

ExpMPOEvolution.time_dependent_H

whether the algorithm supports time-dependent H

class tenpy.algorithms.mpo_evolution.ExpMPOEvolution(psi, model, options, **kwargs)[source]

Bases: TimeEvolutionAlgorithm

Time evolution of an MPS using the W_I or W_II approximation for exp(H dt).

[zaletel2015] described a method to obtain MPO approximations \(W_I\) and \(W_{II}\) for the exponential U = exp(i H dt) of an MPO H, implemented in make_U_I() and make_U_II(). This class uses it for real-time evolution.

Parameters are the same as for Algorithm.

Options

config ExpMPOEvolution

option	summary
approximation	Specifies which approximation is applied. The default 'II' is more precise. [...]
chi_list (from Sweep) in IterativeSweeps.reset_stats	By default (``None``) this feature is disabled. [...]
chi_list_reactivates_mixer (from Sweep) in IterativeSweeps.sweep	If True, the mixer is reset/reactivated each time the bond dimension growth [...]
combine (from Sweep) in Sweep	Whether to combine legs into pipes. This combines the virtual and [...]
compression_method (from ApplyMPO) in MPO.apply	Mandatory. [...]
dt (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Minimal time step by which to evolve.
lanczos_params (from Sweep) in Sweep	Lanczos parameters as described in :cfg:config:`KrylovBased`.
m_temp (from ZipUpApplyMPO) in MPO.apply_zipup	bond dimension will be truncated to `m_temp * chi_max`
max_dt	Threshold for raising errors on too large time steps. Default ``1.0``. [...]
max_hours (from IterativeSweeps) in DMRGEngine.stopping_criterion	If the DMRG took longer (measured in wall-clock time), [...]
max_N_sites_per_ring (from Algorithm) in Algorithm	Threshold for raising errors on too many sites per ring. Default ``18``. [...]
max_sweeps (from IterativeSweeps) in DMRGEngine.stopping_criterion	Maximum number of sweeps to perform.
max_trunc_err (from TimeEvolutionAlgorithm) in TimeDependentHAlgorithm.evolve	Threshold for raising errors on too large truncation errors. Default ``0.01 [...]
min_sweeps (from IterativeSweeps) in DMRGEngine.stopping_criterion	Minimum number of sweeps to perform.
mixer (from Sweep) in DMRGEngine.mixer_activate	Specifies which :class:`Mixer` to use, if any. [...]
mixer_params (from Sweep) in DMRGEngine.mixer_activate	Mixer parameters as described in :cfg:config:`Mixer`.
N_steps (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Number of time steps `dt` to evolve by in :meth:`run`. [...]
order	Order of the algorithm. The total error up to time `t` scales as ``O(t*dt^o [...]
preserve_norm (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Whether the state will be normalized to its initial norm after each time st [...]
start_env (from Sweep) in DMRGEngine.init_env	Number of sweeps to be performed without optimization to update the environment.
start_env_sites (from VariationalCompression) in VariationalCompression	Number of sites to contract for the initial LP/RP environment in case of in [...]
start_time (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Initial value for :attr:`evolved_time`.
start_trunc_err (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Initial truncation error for :attr:`trunc_err`.
tol_theta_diff (from VariationalCompression) in VariationalCompression	Stop after less than `max_sweeps` sweeps if the 1-site wave function change [...]
trunc_params (from ApplyMPO) in MPO.apply	Truncation parameters as described in :cfg:config:`truncation`.
trunc_weight (from ZipUpApplyMPO) in MPO.apply_zipup	reduces cut for Schmidt values to `trunc_weight * svd_min`

option approximation: 'I' | 'II': Specifies which approximation is applied. The default ‘II’ is more precise. See [zaletel2015] and make_U() for more details.

option order: int: Order of the algorithm. The total error up to time t scales as O(t*dt^order). Implemented are order = 1 and order = 2.

option max_dt: float | None: Threshold for raising errors on too large time steps. Default 1.0. See consistency_check(). The trotterization in the time evolution operator assumes that the time step is small. We raise an error if it is not. Can be downgraded to a warning by setting this option to None.

_U

Exponentiated H_MPO;

Type:: list of MPO

_U_param

A dictionary containing the information of the latest created _U. We won’t recalculate _U if those parameters didn’t change.

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.

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().

Options

config TimeEvolutionAlgorithm

option	summary
dt in TimeEvolutionAlgorithm	Minimal time step by which to evolve.
max_N_sites_per_ring (from Algorithm) in Algorithm	Threshold for raising errors on too many sites per ring. Default ``18``. [...]
max_trunc_err in TimeDependentHAlgorithm.evolve	Threshold for raising errors on too large truncation errors. Default ``0.01 [...]
N_steps in TimeEvolutionAlgorithm	Number of time steps `dt` to evolve by in :meth:`run`. [...]
preserve_norm in TimeEvolutionAlgorithm	Whether the state will be normalized to its initial norm after each time st [...]
start_time in TimeEvolutionAlgorithm	Initial value for :attr:`evolved_time`.
start_trunc_err in TimeEvolutionAlgorithm	Initial truncation error for :attr:`trunc_err`.
trunc_params (from Algorithm) in Algorithm	Truncation parameters as described in :cfg:config:`truncation`.

option max_trunc_err: float: Threshold for raising errors on too large truncation errors. Default 0.01. See consistency_check(). When the total accumulated truncation error (its eps) exceeds this value, we raise. Can be downgraded to a warning by setting this option to None.

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 algorithm run 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 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]

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.

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 = False: whether the algorithm supports time-dependent H