Quantum-Inspired Solvers on Mixed-Integer Linear Programming Problem

TL;DR

This paper explores quantum-inspired Ising machine approaches to solving mixed-integer linear programming problems, aiming to overcome computational resource limitations of traditional solvers by leveraging quantum-inspired paradigms.

Contribution

It provides an overview of MILP, details quantum-inspired Ising models, and discusses the potential and challenges of miniaturized quantum-inspired solvers.

Findings

Quantum-inspired Ising machines can solve MILP problems by transforming them into Ising models.

Current quantum-inspired solvers face challenges in scalability and efficiency.

Future miniaturized solvers present opportunities for improved performance.

Abstract

Mixed-integer linear programming (MILP) plays a crucial role in artificial intelligence, biochemistry, finance, cryptography, etc. Notwithstanding popular for decades, the researches of MILP solvers are still limited by the resource consumption caused by complexity and failure of Moore's Law. Quantum-inspired Ising machines, as a new computing paradigm, can be used to solve integer programming problems by reducing them into Ising models. Therefore, it is necessary to understand the technical evolution of quantum inspired solvers to break the bottleneck. In this paper, the concept and traditional algorithms for MILP are introduced. Then, focused on Ising model, the principle and implementations of annealers and coherent Ising machines are summarized. Finally, the paper discusses the challenges and opportunities of miniaturized solvers in the future.