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T'ecnicas Quantum-Inspired en Tensor Networks para Contextos Industriales

arXiv 2404.17645 ↗First posted 8 Mar 2024cs.CE cs.ET cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 62 references that are in the map. First posted on arXiv 8 Mar 2024.

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References · 19

  1. 2020Supervised Learning with Projected Entangled Pair Statescited by 30
  2. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 29
  3. 2024CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networkscited by 29
  4. 2020Anomaly Detection with Tensor Networkscited by 26
  5. 2020Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networkscited by 22
  6. 2020Tensor Networks for Medical Image Classificationcited by 20
  7. 2022TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learningcited by 15
  8. 2022Generative modeling with projected entangled-pair statescited by 14
  9. 2023Tensor Networks for Explainable Machine Learning in Cybersecuritycited by 14
  10. 2020A Multi-Scale Tensor Network Architecture for Classification and Regressioncited by 13
  11. 2015The Singular Value Decomposition, Applications and Beyondcited by 10
  12. 2023Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameterscited by 9
7 more
  1. 2023Efficient tensor network simulation of IBM's largest quantum processorscited by 9
  2. 2022Privacy-preserving machine learning with tensor networkscited by 8
  3. 2022A quantum-inspired tensor network method for constrained combinatorial optimization problemscited by 8
  4. 2021Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecastingcited by 6
  5. 2020Kernelized Support Tensor Train Machinescited by 4
  6. 2021Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computationcited by 2
  7. 2023Mathematical aspects of the Digital Annealer's simulated annealing algorithmcited by 2

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