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Quantum-inspired Techniques in Tensor Networks for Indust...
Alejandro Ma · 2026-05-05 · via cs.LG updates on arXiv.org

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Abstract:In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability.
Comments: 18 pages, 5 figures, improved version with updates for 2026
Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
MSC classes: 81P68, 15A69
ACM classes: G.1.3; G.2.1; I.2; I.4
Cite as: arXiv:2404.11277 [quant-ph]
  (or arXiv:2404.11277v2 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2404.11277

arXiv-issued DOI via DataCite

Submission history

From: Alejandro Mata Ali [view email]
[v1] Wed, 17 Apr 2024 11:34:14 UTC (91 KB)
[v2] Mon, 4 May 2026 16:11:28 UTC (93 KB)