Tensor Networks in a Nutshell

• DOI: 10.48550/arxiv.1708.00006
• Publication Date: 2017-01-01
• Authors: Biamonte, Jacob; Bergholm, Ville
• Keywords: High Energy Physics - Theory; Quantum Physics; FOS: Physical sciences; Disordered Systems and Neural Networks (cond-mat.dis-nn); General Relativity and Quantum Cosmology (gr-qc); Mathematical Physics (math-ph); Condensed Matter - Disordered Systems and Neural Networks; 01 natural sciences; General Relativity and Quantum Cosmology; High Energy Physics - Theory (hep-th); 0103 physical sciences; Quantum Physics (quant-ph); Mathematical Physics

Tensor network methods are taking a central role in modern quantum physics and beyond. They can provide an efficient approximation to certain classes of quantum states, and the associated graphical language makes it easy to describe and pictorially reason about quantum circuits, channels, protocols, open systems and more. Our goal is to explain tensor networks and some associated methods as quickly and as painlessly as possible. Beginning with the key definitions, the graphical tensor network language is presented through examples. We then provide an introduction to matrix product states. We conclude the tutorial with tensor contractions evaluating combinatorial counting problems. The first one counts the number of solutions for Boolean formulae, whereas the second is Penrose's tensor contraction algorithm, returning the number of $3$-edge-colorings of $3$-regular planar graphs.<br/>to appear in Contemporary Physics, 34 pages<br/>