A beginners guide to the practical implementation of tensor networks, including a series of worked tutorials.
Example implementations (coded in Matlab, Julia and Python) of commonly used tensor network algorithms including DMRG, TEBD, MERA and more!
A research blog detailing new developments in the field of tensor networks.
Links to other useful resources for the study or application of tensor networks.
Everything you need to begin your exciting* journey into the world of tensor networks!
(*excitement may vary)
News and Events:
The code examples of tensor network algorithms are in the process of being overhauled with greatly expanded documentation and explanations of their workings (e.g. see TEBD algorithm). Feedback is welcome.
Check out my new project TensorTrace, a graphical interface for developing tensor network based algorithms.
The TensorTrace app (shown left) automatically generates contraction code (in Python, Julia or MATLAB) from the tensor network diagrams that you draw!
What are tensor networks?
Tensor networks are useful constructs for efficiently representing and manipulating correlated data. They work by decomposing high-dimensional data (expressed as a many index tensor) as a product of few index tensors, each of which contains only a relatively small number of parameters.
Originally developed in the context of quantum many-body theory, tensor networks have not only aided in the theoretical understanding of quantum wavefuctions, especially in regards to quantum entanglement, but also form the basis of many powerful numerical simulation approaches.
More recently, tensor networks have found a diverse range of applications in research areas such as quantum gravity and holography, error correcting codes, classical data compression, big data analysis and machine learning.