Quantum-inspired Tensor Network for QUBO, QUDO and Tensor QUDO Problems with k-neighbors
Abstract
This work presents a novel tensor network algorithm for solving Quadratic Unconstrained Binary Optimization (QUBO) problems, Quadratic Unconstrained Discrete Optimization (QUDO) problems, and Tensor Quadratic Unconstrained Discrete Optimization (T-QUDO) problems. The proposed algorithm is based on the MeLoCoToN methodology, which solves combinatorial optimization problems by employing superposition, imaginary time evolution, and projective measurements. Additionally, two different approaches are presented to solve QUBO and QUDO problems with k-neighbors interactions in a lineal chain, one based on 4-order tensor contraction and the other based on matrix-vector multiplication, including sparse computation and a new technique called "Waterfall". Furthermore, the performance of both implementations is compared with a quadratic optimization solver to demonstrate the performance of the method, showing advantages in several problem instances.
Cite
@article{arxiv.2603.28065,
title = {Quantum-inspired Tensor Network for QUBO, QUDO and Tensor QUDO Problems with k-neighbors},
author = {Sergio Muñiz Subiñas and Alejandro Mata Ali and Jorge Martínez Martín and Miguel Franco Hernando and Javier Sedano and Ángel Miguel García-Vico},
journal= {arXiv preprint arXiv:2603.28065},
year = {2026}
}
Comments
14 pages, 16 figures