HackerPapers

arXiv:1912.10049quant-ph

Lectures on Quantum Tensor Networks

Jacob Biamonte

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

Situated as a language between computer science, quantum physics and mathematics, tensor network theory has steadily grown in popularity and can now be found in applications ranging across the entire field of quantum information processing. This book aims to present the best contemporary practices in the use of tensor networks as a reasoning tool, placing quantum states, operators and processes on the same compositional footing. The book has 7 parts and over 40 subsections which took shape in over a decade of teaching. In addition to covering the foundations, the book covers important applications such as matrix product states, open quantum systems and entanglement $-$ all cast into the diagrammatic tensor network language. The intended audience includes those in quantum information science wishing to learn about tensor networks. It includes scientists who have employed tensor networks in their modeling codes who have interest in the tools graphical reasoning capacity. The audience further includes the graduate student researcher, whom with some effort, should find this book accessible. I would appreciate it if you emailed me about any mistakes or typos you find.

arXiv comments
178 page book in LaTeX. Please email me about any mistakes or typos. Current version always available on overleaf, see http://www.overleaf.com/read/jkccbhcdqwnh
Journal reference
DOI
Subjects
quant-phQuantum Physics cond-mat.str-elStrongly Correlated Electrons math-phMathematical Physics math.CTCategory Theory

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Dec 2019 to Dec 2019. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.