# Quantum computing for several AGV scheduling models

## TL;DR

This paper explores using quantum computing to improve scheduling of automated guided vehicles, achieving significant time savings.

## Contribution

The paper introduces quantum computing for AGV scheduling and proposes two new QUBO models solved with an optical quantum computer.

## Key findings

- Quantum computing reduces AGV scheduling computation time by 92% on average.
- Two QUBO models are developed for different scheduling objectives.
- Optical coherent Ising machine effectively solves AGV scheduling problems.

## Abstract

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.