API Outline¶

The TEMPO Collaboration is continuously developing extensions to the original TEMPO algorithm to make this package applicable to a wider set of scenarios (the process tensor approach is one such extension). This calls for a flexible API design to allow to reuse the same objects with different algorithms. We therefore choose an almost fully object oriented approach and separate the code into three layers:

The physical layer: Consists of objects that describe physical quantities, like a system (with a specific Hamiltonian) or the spectral density of an environment.
The algorithms layer: Gathers the information from the physical layer and feeds it (with particular simulation parameters) into the backend. It has the opportunity to make use of specific, extra information from the physical layer.
The backend layer: Is the part of the code where the task has been reduced to a mathematically well defined computation, such as a specific tensor network or an integral. This not only allows to compare the performance of different implementations, it also allows to write hardware specialised implementations (single CPU, cluster, GPU) while keeping the other two layers untouched.

Also, we try to supply some handy utilities such as shortcuts for the Pauli operators for example.

The physical layer¶

class time_evolving_mpo.system.BaseSystem

Abstract class representing a quantum system of interest.

class time_evolving_mpo.system.System: Encodes system Hamiltonian and possibly some additional Markovian decay.
class time_evolving_mpo.system.TimeDependentSystem: Encodes a time dependent system Hamiltonian and possibly some additional time dependent Markovian decay.

class time_evolving_mpo.correlations.BaseCorrelations

Abstract class representing the environments auto-correlations.

class time_evolving_mpo.correlations.CustomCorrelations: Encode an explicitly given environment auto-correlation function.
class time_evolving_mpo.correlations.CustomSD: Encodes the auto-correlations for a given spectral density.
class time_evolving_mpo.correlations.PowerLawSD: Encodes the auto-correlations for a given spectral density of a power law form.

class time_evolving_mpo.bath.Bath

Bundles a time_evolving_mpo.correlations.BaseCorrelations object together with a coupling operator.

The algorithms layer¶

TEMPO¶

class time_evolving_mpo.tempo.TempoParameters

Stores a set of parameters for a TEMPO computation.

class time_evolving_mpo.tempo.Tempo

Class to facilitate a TEMPO computation.

method time_evolving_mpo.tempo.Tempo.compute(): Method that carries out a TEMPO computation.

class time_evolving_mpo.dynamics.Dynamics

Object that encodes the time evolution of a system (with discrete time steps).

function time_evolving_mpo.tempo.guess_tempo_parameters()

Function that chooses an appropriate set of parameters for a particular TEMPO computation.

PT-TEMPO¶

class time_evolving_mpo.pt_tempo.PtTempoParameters

Stores a set of parameters for a PT-TEMPO computation.

class time_evolving_mpo.pt_tempo.PtTempo

Class to facilitate a PT-TEMPO computation.

method time_evolving_mpo.pt_tempo.PtTempo.compute(): Method that carries out a PT-TEMPO computation.

class time_evolving_mpo.process_tensor.ProcessTensor

Object that encodes a so called process tensor (which captures all possible Markovian and non-Markovian interactions between some system and an environment).

The backend layer¶

Currently the only backend available is the 'tensor-network' backend, makes use of the external python package TensorNetwork to carry out the heavy lifting of the tensor network computations. This package itself can, however, be configured to use different tensor network backends (such as “numpy”, “tensorflow” and “pytorch”). All the classes belonging to the algorithm layer allow you to choose the backend and its configuration (with the parameters backend and backend_config).

The default uses:

backend = 'tensor-network'
backend_config = {'backend':'numpy'}

Utillities¶

module time_evolving_mpo.operators: Supplies several commonly used operators, such as the Pauli matrices and spin density matrices.
function time_evolving_mpo.helpers.plot_correlations_with_parameters(): A helper function to plot an auto-correlation function and the sampling points given by a set of parameters for a TEMPO computation.