Engine¶

full name: tenpy.algorithms.tdvp.Engine
parent module: tenpy.algorithms.tdvp
type: class

Inheritance Diagram

Methods

`Engine.__init__`(psi, model, options, **kwargs)
`Engine.get_resume_data`([sequential_simulations])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`Engine.resume_run`()	Resume a run that was interrupted.
`Engine.run`()	(Real-)time evolution with TDVP.
`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.set_anonymous_svd`(U, new_label)	Relabel the svd.
`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.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: tenpy.algorithms.tdvp.TDVPEngine

Deprecated old name of TDVPEngine.

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

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_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) –

set_anonymous_svd(U, new_label)[source]¶

Relabel the svd.

Parameters: U (tenpy.linalg.np_conserved.Array) – the tensor which lacks a leg_label

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

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

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

Lp (tenpy.linalg.np_conserved.Array) – tensor representing the left environment
Rp (tenpy.linalg.np_conserved.Array) – tensor representing the right environment
theta (tenpy.linalg.np_conserved.Array) – the theta tensor which needs to be updated
W (tenpy.linalg.np_conserved.Array) – MPO which is applied to the ‘p0’ leg of theta
W1 (tenpy.linalg.np_conserved.Array) – MPO which is applied to the ‘p1’ leg of theta