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Lectures on Quantum Tensor Networks

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arxiv 1912.10049 v2 pith:72DVVFA2 submitted 2019-12-20 quant-ph cond-mat.str-elmath-phmath.CTmath.MP

classification quant-phcond-mat.str-elmath-phmath.CTmath.MP
keywords quantumtensorbooknetworksincludesapplicationsaudiencefind
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 27 citations worldwide. Full citation record

  1. Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra

    cs.DS 2025-02 conditional novelty 8.0 of 10

    For Kronecker product query oracles, trace and spectral norm estimation require exponentially many queries for all well-conditioned algorithms, and {±1} probe alphabets make zero-testing exponentially weaker than Gaus...

  2. Quantics Tensor Train for solving Gross-Pitaevskii equation

    cond-mat.quant-gas 2025-07 conditional novelty 6.0 of 10

    A quantics tensor train framework solves the 1D Gross-Pitaevskii equation, including multi-species and long-range interactions, with polylogarithmic scaling of storage and operations.

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