The library is being developed in the group of Prof Dieter Jaksch at the University in Oxford. For more information about the group and recent publications, please visit the group website.

We are receiving core support from Prof Chris Greenough's software engineering group at STFC Rutherford Appleton Laboratory, and University dCSE support from Dr Chris Goodyer at NAG.

The library routines are organised in a three tier structure, shown right.

Tier I contains routines for manipulating the tensors that represent the nodes in the network. These routines are completely general and do not depend on network geometry. They include routines for modifying the tensor values through operations on the tensors, changing how the nodes are connected to one another in the network, and getting certain values (e.g. diagonal values) of the tensors. All these routines are contained in the core library libtnt.a, and they can be used to build your own custom TNT algorithms, or in fact use in any other application where tensor manipulations are required. The figure shows two example operations on network nodes that are commonly used in TNT algorithms: firstly node A is contracted along the physical leg with its complex conjugate, and secondly node B is factorised into three new nodes using a singular value decomposition.

Tier II contains routines that operate on a network. A few of these (e.g. copying a network, deleting a network) do not depend on network geometry and these are also included in the core library. The remaining routines are specific to the network geometry e.g. a matrix product state (MPS) network. These routines contain building blocks for building the TNT algorithms for these networks types, for example contracting an entire network to find an expectation value on a given site, or applying a sequence of two-site gates.

Tier III contains complete algorithms. For example a given algorithm may load an MPS start state and Hamiltonian from an initialisation file, time-evolve the MPS under the Hamiltonian for a given time t, calculating expectation values at given time-intervals. These algorithms can either be used without modification, or can be easily changed to build your own custom routines.

Currently availably for download is the alpha version of the core pre-compiled library libtnt.a, which contains optimised tier I routines and general tier II routines. Also available are many routines for manipulating and time-evolving an MPS. These routines are being used and tested within Dieter Jaksch's group.

A beta version of the core library libtnt.a will be released soon, along with an tier II MPS library libtnt_mps.a, which will contain a variety of routines for manipulating MPS networks. Algorithms will be provided for time-evolving a start state using time-evolving block decimation, and finding the ground state using a variational approach. For more details see the section 'Status of beta version' at the bottom of the page.

In the near future, development will focus on optimising the core library and the MPS library, parallelising the core tensor operations. This will include treatment of global physical symmetries, leading to dramatic speed-ups in systems where there are conserved quantities. This work is being carried out with support under the University dCSE scheme from HECToR, and will be completed by July 2013.

During this time, as well as continual optimisations to the core library, new routines will continually be added to the MPS library, taking into account feature requests and feedback from users.

After this, development will focus on adding to the tier II libraries, providing libraries for different network types (e.g. MERA, PEPS) moving to concentrating on TNT algorithms in two dimensions. These will first be provided in a serial library, before highly optimised libraries are released with parallelism implemented at the network and algorithm level as well as utilising the already parallelised core tensor routines.

To get hold of a copy of the library, please first join the project by clicking the 'Join this Project' button on the top right. If you do not already have an account on CCPForge, you will need to do this first.

After this, you will be able to download the pre-compiled library from the Files section. More detailed information using the library is available in the documentation, which is also available for download here.

The source code is also available on request, please contact support@tensornetworktheory.org for access.

To keep up to date, please join the tntlibrary-users mailing list by subscribing here. You will then be informed whenever a new version of the library is available.

We would appreciate any feedback you have on the TNT library, to help us develop stable code that is of use to as many researchers as possible.

If you think there are any features that would be useful, that are not already described in the 'Plans for development section', e.g. an operation on a node/network, or an option for input or output of data, please request it in the Feature Request tracker. We will change the status to let you know if your feature is added to the list of features that developers are working on, if it is in progress, or if it is complete.

If you notice a bug, please report it in the Bug Report tracker with as much detail as you can, and we will work to resolve it as soon as possible.

For any other comments or questions please contact support@tensornetworktheory.org.

The current functions have library has been tested primarily on Linux systems using performance compilers (Intel) and linear algebra libraries (Intel MKL and NAG). Before the beta version is released we will complete development of versions that can be used on Mac, Linux and Windows environments using freely available libraries and compilers. Although this version will not be as highly optimised, we envisage that it will be useful for testing and initial development, before switching to the high performance version for treating large problems, for use on HPC systems.

The following functions (with current status indicated) will be included in the beta version:

Core library functions
SVD of a general node in the network: complete
Contraction of a pair of nodes in the network: complete
Solution of the standard eigenvalue problem using any contraction pattern: developed - in testing
Input and output of Matlab data: complete
Input and output of data in a 'tnt' format: in development
Note - These core tensor manipulation routines will also be further updated to include optimisations made as part of the University dCSE project.

Functions for manipulating general networks
Loading and saving a network: complete
Copying a network: complete
Deleting a network: complete
Splitting a network at any point: complete
Manipulating a network by inserting and removing network nodes: complete
Creating a new network: developed - in testing

Library functions for MPS
Test functions for the following have been developed, but need to be assembled in a general form for inclusion into a tier II static library.
TEBD sweep: The current version includes options for spatially/time dependent operators and static operators.
VMPS sweep: This refers to a one-site optimisation by solving the standard eigenvalue problem. The current version allows an efficient contraction pattern to be used for the one-site optimisation.
DMRG sweep: This refers to a two-site optimisation by solving the stardard eigenvalue problem, with a truncated SVD to return to the MPS form. In development.
Creating MPOs: Routines to insert and determine expecation values using MPO (matrix product operators) have been developed, loading operators from initialisation files. These will be extended to generate MPOs for all common on-site and nearest-neighbour operators.
Calculating expectation values: Routines exist to calculate expectation values of one-site, two-site and site-wide operators. They include options to perform a more efficient normalisation in the case that the MPS is in the canonical form. They will be generalised to allow expectation values for any multi-site operator.

Pre-compiled algorithms
The beta version will contain precompiled algorithms (tier III) for TEBD (with real and imaginary time evolution), VMPS and DMRG algorithms. They will run directly, with the system parameters specified by options on the command line, or by choosing suitable initialisation files. Simplified versions will also be available as source code, for modification by users.