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.

翻译：量子计算在处理复杂问题时具有超越当前经典计算机的潜力。组合优化已成为量子计算机的关键目标领域之一，因为该领域中的问题在众多工业应用场景中扮演着重要角色（例如，优化制造运营或改进决策流程）。目前，已有不同类型的优化高性能软件（如ILOG CPLEX和Gurobi）支持工程师和科学家利用经典计算机解决优化问题。为了使用量子资源，用户需要具备量子算法、SDK及库等领域的专业知识，这对于希望将该技术融入工作流的从业者而言构成限制。我们的目标是在经典优化软件包中增加软件基础设施，使应用开发者在搭建工作流时能够便捷地对接量子平台。本文提出了一种通过经典接口无缝使用量子资源的工具。我们的方法包括一个Python库扩展，该扩展提供后端以支持访问多个量子提供商。我们的流水线使优化软件开发者能够选择性实验量子资源，并评估混合量子-经典优化解决方案的性能提升。