{"title":"Hyperoptimized approximate contraction of tensor networks for rugged-energy-landscape spin glasses on periodic square and cubic lattices","authors":"Adil A. Gangat, Johnnie Gray","doi":"arxiv-2407.21287","DOIUrl":null,"url":null,"abstract":"Obtaining the low-energy configurations of spin glasses that have rugged\nenergy landscapes is of direct relevance to combinatorial optimization and\nfundamental science. Search-based heuristics have difficulty with this task due\nto the existence of many local minima that are far from optimal. The work of\n[M. M. Rams et al., Phys. Rev. E 104, 025308 (2021)] demonstrates an\nalternative that can bypass this issue for spin glasses with planar or\nquasi-planar geometry: sampling the Boltzmann distribution via approximate\ncontractions of tensor networks. The computational complexity of this approach\nis due only to the complexity of contracting the network, and is therefore\nindependent of landscape ruggedness. Here we initiate an investigation of how\nto take this approach beyond (quasi-)planar geometry by utilizing\nhyperoptimized approximate contraction of tensor networks [J. Gray and G. K.-L.\nChan, Phys. Rev. X 14, 011009 (2024)]. We perform tests on the periodic square-\nand cubic-lattice, planted-solution Ising spin glasses generated with tile\nplanting [F. Hamze et al., Phys. Rev. E 97, 043303 (2018)] for up to 2304\n(square lattice) and 216 (cubic lattice) spins. For a fixed bond dimension, the\ntime complexity is quadratic. With a bond dimension of only four, over the\ntested system sizes the average solution quality in the most rugged instance\nclass remains at ~1% (square lattice) or ~10% (cubic lattice) of optimal. These\nresults encourage further investigation of tensor network contraction for\nrugged-energy-landscape spin-glass problems, especially given that this\napproach is not limited to the Ising (i.e., binary) or two-body (i.e.,\nquadratic) settings.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":"78 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Disordered Systems and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.21287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Obtaining the low-energy configurations of spin glasses that have rugged
energy landscapes is of direct relevance to combinatorial optimization and
fundamental science. Search-based heuristics have difficulty with this task due
to the existence of many local minima that are far from optimal. The work of
[M. M. Rams et al., Phys. Rev. E 104, 025308 (2021)] demonstrates an
alternative that can bypass this issue for spin glasses with planar or
quasi-planar geometry: sampling the Boltzmann distribution via approximate
contractions of tensor networks. The computational complexity of this approach
is due only to the complexity of contracting the network, and is therefore
independent of landscape ruggedness. Here we initiate an investigation of how
to take this approach beyond (quasi-)planar geometry by utilizing
hyperoptimized approximate contraction of tensor networks [J. Gray and G. K.-L.
Chan, Phys. Rev. X 14, 011009 (2024)]. We perform tests on the periodic square-
and cubic-lattice, planted-solution Ising spin glasses generated with tile
planting [F. Hamze et al., Phys. Rev. E 97, 043303 (2018)] for up to 2304
(square lattice) and 216 (cubic lattice) spins. For a fixed bond dimension, the
time complexity is quadratic. With a bond dimension of only four, over the
tested system sizes the average solution quality in the most rugged instance
class remains at ~1% (square lattice) or ~10% (cubic lattice) of optimal. These
results encourage further investigation of tensor network contraction for
rugged-energy-landscape spin-glass problems, especially given that this
approach is not limited to the Ising (i.e., binary) or two-body (i.e.,
quadratic) settings.