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

arXiv: https://arxiv.org/abs/2404.17645 · HTML: https://mlsanity.com/paper/2404.17645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [Tensor Networks Meet Neural Networks: A Survey and Future Perspectives](https://mlsanity.com/paper/2302.09019/) (2023)
- [CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/2401.14109/) (2024)
- [Supervised Learning with Projected Entangled Pair States](https://mlsanity.com/paper/2009.09932/) (2020)
- [Anomaly Detection with Tensor Networks](https://mlsanity.com/paper/2006.02516/) (2020)
- [Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/2007.00017/) (2020)
- [Tensor Networks for Medical Image Classification](https://mlsanity.com/paper/2004.10076/) (2020)
- [Generative modeling with projected entangled-pair states](https://mlsanity.com/paper/2202.08177/) (2022)
- [TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning](https://mlsanity.com/paper/2205.00293/) (2022)
- [A Multi-Scale Tensor Network Architecture for Classification and Regression](https://mlsanity.com/paper/2001.08286/) (2020)
- [Tensor Networks for Explainable Machine Learning in Cybersecurity](https://mlsanity.com/paper/2401.00867/) (2023)
- [The Singular Value Decomposition, Applications and Beyond](https://mlsanity.com/paper/1510.08532/) (2015)
- [Privacy-preserving machine learning with tensor networks](https://mlsanity.com/paper/2202.12319/) (2022)
- [Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters](https://mlsanity.com/paper/2305.06058/) (2023)
- [Efficient tensor network simulation of IBM's largest quantum processors](https://mlsanity.com/paper/2309.15642/) (2023)
- [A quantum-inspired tensor network method for constrained combinatorial optimization problems](https://mlsanity.com/paper/2203.15246/) (2022)
- [Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting](https://mlsanity.com/paper/2105.04983/) (2021)
- [Kernelized Support Tensor Train Machines](https://mlsanity.com/paper/2001.00360/) (2020)
- [Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation](https://mlsanity.com/paper/2101.04194/) (2021)
- [Mathematical aspects of the Digital Annealer's simulated annealing algorithm](https://mlsanity.com/paper/2303.08392/) (2023)

## 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.