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

Methods
|
|
|
Calculate |
|
Evolve by N_steps*dt. |
|
|
Return necessary data to resume a |
|
Prepare an evolution step. |
|
Resume a run that was interrupted. |
|
Perform a (real-)time evolution of |
|
|
Perform a (real-)time evolution of |
|
Initialize algorithm from another algorithm instance of a different class. |
Class Attributes and Properties
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 inmake_U_I()
andmake_U_II()
. This class uses it for real-time evolution.Parameters are the same as for
Algorithm
.Options
- config ExpMPOEvolution¶
option summary Specifies which approximation is applied. The default 'II' is more precise. [...]
By default (``None``) this feature is disabled. [...]
chi_list_reactivates_mixer (from Sweep) in Sweep.sweep
If True, the mixer is reset/reactivated each time the bond dimension growth [...]
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.
init_env_data (from Sweep) in DMRGEngine.init_env
Dictionary as returned by ``self.env.get_initialization_data()`` from [...]
lanczos_params (from Sweep) in Sweep
Lanczos parameters as described in :cfg:config:`Lanczos`.
m_temp (from ZipUpApplyMPO) in MPO.apply_zipup
bond dimension will be truncated to `m_temp * chi_max`
max_sweeps (from VariationalCompression) in VariationalCompression
Minimum and maximum number of sweeps to perform for the compression.
min_sweeps (from VariationalCompression) in VariationalCompression
Minimum and maximum number of sweeps to perform for the compression.
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 of the algorithm. The total error up to time `t` scales as ``O(t*dt^o [...]
orthogonal_to (from Sweep) in DMRGEngine.init_env
Deprecated in favor of the `orthogonal_to` function argument (forwarded fro [...]
preserve_norm (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm
Whether the state will be normalized to its initial norm after each time st [...]
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 inital LP/RP environment in case of inf [...]
start_time (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm
Initial value for :attr:`evolved_time`.
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 start_trunc_err:
TruncationError
¶ Initial truncation error for
trunc_err
- 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 start_trunc_err:
- evolved_time¶
Indicating how long psi has been evolved,
psi = exp(-i * evolved_time * H) psi(t=0)
.- Type
- trunc_err¶
The error of the represented state which is introduced due to the truncation during the sequence of update steps
- Type
- _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
- 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.
- calc_U(dt, order=2, approximation='II')[source]¶
Calculate
self._U_MPO
.This function calculates the approximation
U ~= exp(-i dt_ H)
withdt_ = dt` for ``order=1
, ordt_ = (1 - 1j)/2 dt
anddt_ = (1 + 1j)/2 dt
fororder=2
.
- 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
- 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
- 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()
.
- 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
.
- 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()
.
- time_dependent_H = False¶
whether the algorithm supports time-dependent H