QPLEX: Realizing the Integration of Quantum Computing into Combinatorial Optimization Software

TL;DR

This paper introduces QPLEX, a Python library extension that enables classical combinatorial optimization software to seamlessly interface with quantum computing resources, facilitating hybrid quantum-classical optimization workflows.

Contribution

It presents a software infrastructure that integrates quantum computing into existing classical optimization tools without requiring domain-specific quantum knowledge.

Findings

Enables easy access to multiple quantum providers from classical software

Facilitates experimentation with hybrid quantum-classical optimization solutions

Supports performance assessment of quantum-enhanced optimization methods

Abstract

Quantum computing has the potential to surpass the capabilities of current classical computers when solving complex problems. Combinatorial optimization has emerged as one of the key target areas for quantum computers as problems found in this field play a critical role in many different industrial application sectors (e.g., enhancing manufacturing operations or improving decision processes). Currently, there are different types of high-performance optimization software (e.g., ILOG CPLEX and Gurobi) that support engineers and scientists in solving optimization problems using classical computers. In order to utilize quantum resources, users require domain-specific knowledge of quantum algorithms, SDKs and libraries, which can be a limiting factor for any practitioner who wants to integrate this technology into their workflows. Our goal is to add software infrastructure to a classical optimization package so that application developers can interface with quantum platforms readily when setting up their workflows. This paper presents a tool for the seamless utilization of quantum resources through a classical interface. Our approach consists of a Python library extension that provides a backend to facilitate access to multiple quantum providers. Our pipeline enables optimization software developers to experiment with quantum resources selectively and assess performance improvements of hybrid quantum-classical optimization solutions.