LayerwiseEvolver
class LayerwiseEvolver(evolution_state, layers, *args, **kwargs)
Bases: TEBDEvolver
A special case of the TEBDEvolver based on layer-wise evolution models.
As also explained in tenpy_layers, this implementation extracts the alternating even/odd bond updates implemented inside of the original TEBDEngine to become the end users responsibility. It does so, by replacing the single Hamiltonian provided to the TEBDEvolver instance with a sequence of LayerModel instances. Every single instance of these encodes a single layer of interactions. These should enforce the alternating updates of even and odd bonds of the underlying tensor network.
The motivation for this more complicated interface is that is provides a lot more flexbility and enables users to define custom Trotter product formulas rather than being limited to the ones implemented by TeNPy directly.
Initialize a LayerwiseEvolver instance.
Parameters
- evolution_state (tenpy_tebd.MPOState) – forwarded to
TEBDEvolver. Please refer to its documentation for more details. - layers (list[LayerModel]) – the list of models describing single layers of interactions. See above as well as the explanations provided in
tenpy_layers. - args – any further positional arguments will be forwarded to the
TEBDEvolverconstructor. - kwargs – any further keyword arguments will be forwarded to the
TEBDEvolverconstructor.
Attributes
layers
The layers of interactions used to implement the time-evolution.
dt
The time step to be used by this time-evolution instance.
Methods
calc_U
calc_U(order, delta_t, type_evo='real', E_offset=None)
Calculates the local bond updates.
This adapts calc_U() to work with the layer-wise implementation.
Parameters
- order (int) – this is being ignored.
- delta_t (float) – the time-step to use.
- type_evo (str) – the type of time-evolution. Imaginary time-evolution is not supported at this time.
- E_offset (list[float] | None) – a constant energy offset to be applied.
Return type
None
evolve
evolve(N_steps, dt)
Perform a single time step of TEBD.
Parameters
- N_steps (int) – should always be
1in this case. SeeTEBDEnginefor more details. - dt (float) – the time-step to use.
Returns
The truncation error.
Return type
suzuki_trotter_decomposition
static suzuki_trotter_decomposition(order, N_steps)
Returns an empty list.
This method is undefined for this subclass but we cannot raise an error upon calling it because of the internal algorithm flow. Instead, the Trotter decomposition in this class is encoded directly into the layers.
Parameters
Returns
An empty list.
Return type
suzuki_trotter_time_steps
static suzuki_trotter_time_steps(order)
Returns an empty list.
This method is undefined for this subclass but we cannot raise an error upon calling it because of the internal algorithm flow. Instead, the Trotter decomposition in this class is encoded directly into the layers.
Parameters
order (int) – is being ignored.
Returns
An empty list.
Return type