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A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions and Gauge Freedom

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arxiv 2202.02138 v1 pith:3KFWKX3I submitted 2022-02-04 quant-ph cond-mat.str-el

classification quant-phcond-mat.str-el
keywords tensornetworknetworksdecompositionsfreedomgaugemethodspractical
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We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically, which is intended to facilitate the practical application of tensor network methods for researchers that are already versed with their theoretical foundations. These skills include an introduction to the contraction of tensor networks, to optimal tensor decompositions, and to the manipulation of gauge degrees of freedom in tensor networks. The topics presented are of key importance to many common tensor network algorithms such as DMRG, TEBD, TRG, PEPS and MERA.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 2 citations worldwide. Full citation record

  1. Advantages of density in tensor network geometries for gradient based training

    quant-ph 2024-12 conditional novelty 6.0 of 10

    Densely connected tensor network geometries train to lower infidelity than sparse ones on random quantum states, and a new leaf-contraction trick reduces memory while improving training.

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