Tensor Network Based HOBO Solver

TL;DR

This paper introduces a new tensor network-based solver tailored for higher-order binary optimization problems in quantum computing, addressing limitations of existing QUBO solvers for complex, high-dimensional tasks.

Contribution

The paper presents a novel tensor network approach specifically designed to efficiently solve HOBO problems, expanding capabilities beyond traditional QUBO solvers.

Findings

Effective management of high-dimensional optimization complexity

Potential for broad applications in quantum computing

Promising extension possibilities for problem formulation

Abstract

In the field of quantum computing, combinatorial optimization problems are typically addressed using QUBO (Quadratic Unconstrained Binary Optimization) solvers. However, these solvers are often insufficient for tackling higher-order problems. In this paper, we introduce a novel and efficient solver designed specifically for HOBO (Higher-Order Binary Optimization) problem settings. Our approach leverages advanced techniques to effectively manage the complexity and computational demands associated with high-dimensional optimization tasks. The proposed solver is a promising tool with significant potential for future extensions in terms of formulation. This solver holds promising potential for a wide range of applications in quantum computing.