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

TL;DR

This paper investigates the use of quantum-inspired algorithms within tensor networks for industrial applications, analyzing their feasibility, limitations, and potential scalability based on existing literature and use cases.

Contribution

It provides a comprehensive review and analysis of quantum-inspired tensor network techniques applied to industrial contexts, highlighting their capabilities and constraints.

Findings

Quantum-inspired tensor network methods show promise for industrial problems.

Limitations exist in scalability and applicability to large-scale industrial data.

The study identifies key use cases where these techniques are most effective.

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.