Engine

Inheritance Diagram

Inheritance diagram of tenpy.algorithms.tdvp.Engine

Methods

Engine.__init__(psi, model, options, **kwargs)

Engine.evolve(N_steps, dt)

Evolve by N_steps*dt.

Engine.evolve_step(dt)

Engine.get_resume_data([sequential_simulations])

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

Engine.prepare_evolve(dt)

Prepare an evolution step.

Engine.resume_run()

Resume a run that was interrupted.

Engine.run()

(Real-)time evolution with TDVP.

Engine.run_evolution(N_steps, dt)

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

Engine.run_one_site([N_steps])

Run the TDVP algorithm with the one site algorithm.

Engine.run_two_sites([N_steps])

Run the TDVP algorithm with two sites update.

Engine.sweep_left_right()

Performs the sweep left->right of the second order TDVP scheme with one site update.

Engine.sweep_left_right_two()

Performs the sweep left->right of the second order TDVP scheme with two sites update.

Engine.sweep_right_left()

Performs the sweep right->left of the second order TDVP scheme with one site update.

Engine.sweep_right_left_two()

Performs the sweep left->right of the second order TDVP scheme with two sites update.

Engine.switch_engine(other_engine, *[, options])

Initialize algorithm from another algorithm instance of a different class.

Engine.theta_svd_left_right(theta)

Performs the SVD from left to right.

Engine.theta_svd_right_left(theta)

Performs the SVD from right to left.

Engine.update_s_h0(s, H, dt)

Update with the zero site Hamiltonian (update of the singular value)

Engine.update_theta_h1(Lp, Rp, theta, W, dt)

Update with the one site Hamiltonian.

Engine.update_theta_h2(Lp, Rp, theta, W0, W1, dt)

Update with the two sites Hamiltonian.

Class Attributes and Properties

Engine.TDVP_params

Engine.time_dependent_H

whether the algorithm supports time-dependent H

Engine.verbose

class tenpy.algorithms.tdvp.Engine(psi, model, options, **kwargs)[source]

Bases: OldTDVPEngine

Deprecated old name of the OldTDVPEngine.

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.

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().

run()[source]

(Real-)time evolution with TDVP.

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.

run_one_site(N_steps=None)[source]

Run the TDVP algorithm with the one site algorithm.

Warning

Be aware that the bond dimension will not increase!

Parameters

N_steps (integer. Number of steps) –

run_two_sites(N_steps=None)[source]

Run the TDVP algorithm with two sites update.

The bond dimension will increase. Truncation happens at every step of the sweep, according to the parameters set in trunc_params.

Parameters

N_steps (integer. Number of steps) –

sweep_left_right()[source]

Performs the sweep left->right of the second order TDVP scheme with one site update.

Evolve from 0.5*dt.

sweep_left_right_two()[source]

Performs the sweep left->right of the second order TDVP scheme with two sites update.

Evolve from 0.5*dt

sweep_right_left()[source]

Performs the sweep right->left of the second order TDVP scheme with one site update.

Evolve from 0.5*dt

sweep_right_left_two()[source]

Performs the sweep left->right of the second order TDVP scheme with two sites update.

Evolve from 0.5*dt

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().

theta_svd_left_right(theta)[source]

Performs the SVD from left to right.

Parameters

theta (tenpy.linalg.np_conserved.Array) – the theta tensor on which the SVD is applied

theta_svd_right_left(theta)[source]

Performs the SVD from right to left.

Parameters

theta (tenpy.linalg.np_conserved.Array,) – The theta tensor on which the SVD is applied

time_dependent_H = False

whether the algorithm supports time-dependent H

update_s_h0(s, H, dt)[source]

Update with the zero site Hamiltonian (update of the singular value)

Parameters

  • s (tenpy.linalg.np_conserved.Array) – representing the singular value matrix which is updated

  • H (H0_mixed) – zero site Hamiltonian that we need to apply on the singular value matrix

  • dt (complex number) – time step of the evolution

update_theta_h1(Lp, Rp, theta, W, dt)[source]

Update with the one site Hamiltonian.

Parameters

  • Lp (Array) – tensor representing the left environment

  • Rp (Array) – tensor representing the right environment

  • theta (Array) – the theta tensor which needs to be updated

  • W (Array) – MPO which is applied to the ‘p’ leg of theta

update_theta_h2(Lp, Rp, theta, W0, W1, dt)[source]

Update with the two sites Hamiltonian.

Parameters