# Quantum-inspired Techniques in Tensor Networks for Industrial Contexts

arXiv: https://arxiv.org/abs/2404.11277 · HTML: https://mlsanity.com/paper/2404.11277/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 79 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [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)
- [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)
- [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)
- [TensorKrowch: Smooth integration of tensor networks in machine learning](https://mlsanity.com/paper/2306.08595/) (2023)
- [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)

## Built on by (most cited first, 2 of 2)

- [PEPS: Quantum-Inspired Reinforcement Learning for Coherent Reasoning Traces in LLMs](https://mlsanity.com/paper/2509.20105/) (2025)
- [Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors](https://mlsanity.com/paper/2603.12540/) (2026)

## 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.