TEBDEngine¶

full name: tenpy.algorithms.tebd.TEBDEngine
parent module: tenpy.algorithms.tebd
type: class

Inheritance Diagram

Methods

`TEBDEngine.__init__`(psi, model, options, ...)
`TEBDEngine.calc_U`(order, delta_t[, ...])	Calculate `self.U_bond` from `self.bond_eig_{vals,vecs}`.
`TEBDEngine.get_resume_data`([...])	Return necessary data to resume a `run()` interrupted at a checkpoint.
`TEBDEngine.resume_run`()	Resume a run that was interrupted.
`TEBDEngine.run`()	Run TEBD real time evolution by N_steps`*`dt.
`TEBDEngine.run_GS`()	TEBD algorithm in imaginary time to find the ground state.
`TEBDEngine.suzuki_trotter_decomposition`(...)	Returns list of necessary steps for the suzuki trotter decomposition.
`TEBDEngine.suzuki_trotter_time_steps`(order)	Return time steps of U for the Suzuki Trotter decomposition of desired order.
`TEBDEngine.update`(N_steps)	Evolve by `N_steps * U_param['dt']`.
`TEBDEngine.update_bond`(i, U_bond)	Updates the B matrices on a given bond.
`TEBDEngine.update_bond_imag`(i, U_bond)	Update a bond with a (possibly non-unitary) U_bond.
`TEBDEngine.update_imag`(N_steps)	Perform an update suitable for imaginary time evolution.
`TEBDEngine.update_step`(U_idx_dt, odd)	Updates either even or odd bonds in unit cell.

Class Attributes and Properties

`TEBDEngine.TEBD_params`
`TEBDEngine.time_dependent_H`	whether the algorithm supports time-dependent H
`TEBDEngine.trunc_err_bonds`	truncation error introduced on each non-trivial bond.
`TEBDEngine.verbose`

class tenpy.algorithms.tebd.TEBDEngine(psi, model, options, **kwargs)[source]¶

Bases: tenpy.algorithms.algorithm.TimeEvolutionAlgorithm

Time Evolving Block Decimation (TEBD) algorithm.

Parameters are the same as for Algorithm.

Deprecated since version 0.6.0: Renamed parameter/attribute TEBD_params to options.

Options

config TEBDEngine¶

option	summary
delta_tau_list in PurificationTEBD.run_GS	A list of floats: the timesteps to be used. [...]
dt (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Minimal time step by which to evolve.
N_steps in PurificationTEBD.run_GS	Number of steps before measurement can be performed
order in PurificationTEBD.run_GS	Order of the Suzuki-Trotter decomposition.
start_time (from TimeEvolutionAlgorithm) in TimeEvolutionAlgorithm	Initial value for :attr:`evolved_time`.
start_trunc_err	Initial truncation error for :attr:`trunc_err`.
trunc_params (from Algorithm) in Algorithm	Truncation parameters as described in :cfg:config:`truncation`.

option start_trunc_err: TruncationError¶: Initial truncation error for trunc_err.

option order: int¶: Order of the algorithm. The total error for evolution up to a fixed time t scales as O(t*dt^order).

trunc_err¶

The error of the represented state which is introduced due to the truncation during the sequence of update steps.

Type: TruncationError

psi¶

The MPS, time evolved in-place.

Type: MPS

model¶

The model defining the Hamiltonian.

Type: NearestNeighborModel

_U¶

Exponentiated H_bond (bond Hamiltonians), i.e. roughly exp(-i H_bond dt_i). First list for different dt_i as necessary for the chosen order, second list for the L different bonds.

Type: list of list of Array

_U_param¶

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

Type: dict

_trunc_err_bonds¶

The local truncation error introduced at each bond, ignoring the errors at other bonds. The i-th entry is left of site i.

Type: list of TruncationError

_update_index¶

The indices i_dt,i_bond of U_bond = self._U[i_dt][i_bond] during update_step.

Type: None | (int, int)

property trunc_err_bonds¶: truncation error introduced on each non-trivial bond.

run()[source]¶: Run TEBD real time evolution by N_steps`*`dt.

run_GS()[source]¶

TEBD algorithm in imaginary time to find the ground state.

Note

It is almost always more efficient (and hence advisable) to use DMRG. This algorithms can nonetheless be used quite well as a benchmark and for comparison.

option TEBDEngine.delta_tau_list: list¶: A list of floats: the timesteps to be used. Choosing a large timestep delta_tau introduces large (Trotter) errors, but a too small time step requires a lot of steps to reach exp(-tau H) --> |psi0><psi0|. Therefore, we start with fairly large time steps for a quick time evolution until convergence, and the gradually decrease the time step.

option TEBDEngine.order: int¶: Order of the Suzuki-Trotter decomposition.

option TEBDEngine.N_steps: int¶: Number of steps before measurement can be performed

static suzuki_trotter_time_steps(order)[source]¶

Return time steps of U for the Suzuki Trotter decomposition of desired order.

See suzuki_trotter_decomposition() for details.

Parameters: order (int) – The desired order of the Suzuki-Trotter decomposition.
Returns: time_steps – We need U = exp(-i H_{even/odd} delta_t * dt) for the dt returned in this list.
Return type: list of float

static suzuki_trotter_decomposition(order, N_steps)[source]¶

Returns list of necessary steps for the suzuki trotter decomposition.

We split the Hamiltonian as \(H = H_{even} + H_{odd} = H[0] + H[1]\). The Suzuki-Trotter decomposition is an approximation \(\exp(t H) \approx prod_{(j, k) \in ST} \exp(d[j] t H[k]) + O(t^{order+1 })\).

Parameters: order (1, 2, 4, '4_opt') – The desired order of the Suzuki-Trotter decomposition. Order 1 approximation is simply \(e^A a^B\). Order 2 is the “leapfrog” e^{A/2} e^B e^{A/2}. Order 4 is the fourth-order from [suzuki1991] (also referenced in [schollwoeck2011]), and '4_opt' gives the optmized version of Equ. (30a) in [barthel2020].
Returns: ST_decomposition – Indices j, k of the time-steps d = suzuki_trotter_time_step(order) and the decomposition of H. They are chosen such that a subsequent application of exp(d[j] t H[k]) to a given state |psi> yields (exp(N_steps t H[k]) + O(N_steps t^{order+1}))|psi>.
Return type: list of (int, int)

calc_U(order, delta_t, type_evo='real', E_offset=None)[source]¶

Calculate self.U_bond from self.bond_eig_{vals,vecs}.

This function calculates

U_bond = exp(-i dt (H_bond-E_offset_bond)) for type_evo='real', or
U_bond = exp(- dt H_bond) for type_evo='imag'.

For first order (in delta_t), we need just one dt=delta_t. Higher order requires smaller dt steps, as given by suzuki_trotter_time_steps().

Parameters

order (int) – Trotter order calculated U_bond. See update for more information.
delta_t (float) – Size of the time-step used in calculating U_bond
type_evo ('imag' | 'real') – Determines whether we perform real or imaginary time-evolution.
E_offset (None | list of float) – Possible offset added to H_bond for real-time evolution.

update(N_steps)[source]¶

Evolve by N_steps * U_param['dt'].

Parameters: N_steps (int) – The number of steps for which the whole lattice should be updated.
Returns: trunc_err – The error of the represented state which is introduced due to the truncation during this sequence of update steps.
Return type: TruncationError

update_step(U_idx_dt, odd)[source]¶

Updates either even or odd bonds in unit cell.

Depending on the choice of p, this function updates all even (E, odd=False,0) or odd (O) (odd=True,1) bonds:

|     - B0 - B1 - B2 - B3 - B4 - B5 - B6 -
|       |    |    |    |    |    |    |
|       |    |----|    |----|    |----|
|       |    |  E |    |  E |    |  E |
|       |    |----|    |----|    |----|
|       |----|    |----|    |----|    |
|       |  O |    |  O |    |  O |    |
|       |----|    |----|    |----|    |

Note that finite boundary conditions are taken care of by having Us[0] = None.

Parameters

U_idx_dt (int) – Time step index in self._U, evolve with Us[i] = self.U[U_idx_dt][i] at bond (i-1,i).
odd (bool/int) – Indication of whether to update even (odd=False,0) or even (odd=True,1) sites

Returns

trunc_err – The error of the represented state which is introduced due to the truncation during this sequence of update steps.

Return type

TruncationError

update_bond(i, U_bond)[source]¶

Updates the B matrices on a given bond.

Function that updates the B matrices, the bond matrix s between and the bond dimension chi for bond i. The correponding tensor networks look like this:

|           --S--B1--B2--           --B1--B2--
|                |   |                |   |
|     theta:     U_bond        C:     U_bond
|                |   |                |   |

Parameters

i (int) – Bond index; we update the matrices at sites i-1, i.
U_bond (Array) – The bond operator which we apply to the wave function. We expect labels 'p0', 'p1', 'p0*', 'p1*'.

Returns

trunc_err – The error of the represented state which is introduced by the truncation during this update step.

Return type

TruncationError

update_imag(N_steps)[source]¶

Perform an update suitable for imaginary time evolution.

Instead of the even/odd brick structure used for ordinary TEBD, we ‘sweep’ from left to right and right to left, similar as DMRG. Thanks to that, we are actually able to preserve the canonical form.

Parameters: N_steps (int) – The number of steps for which the whole lattice should be updated.
Returns: trunc_err – The error of the represented state which is introduced due to the truncation during this sequence of update steps.
Return type: TruncationError

update_bond_imag(i, U_bond)[source]¶

Update a bond with a (possibly non-unitary) U_bond.

Similar as update_bond(); but after the SVD just keep the A, S, B canonical form. In that way, one can sweep left or right without using old singular values, thus preserving the canonical form during imaginary time evolution.

Parameters

i (int) – Bond index; we update the matrices at sites i-1, i.
U_bond (Array) – The bond operator which we apply to the wave function. We expect labels 'p0', 'p1', 'p0*', 'p1*'.

Returns

trunc_err – The error of the represented state which is introduced by the truncation during this update step.

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

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().