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Quantum computing has the potential to surpass the capabilities of currentclassical computers when solving complex problems. Combinatorial optimizationhas emerged as one of the key target areas for quantum computers as problemsfound in this field play a critical role in many different industrialapplication sectors (e.g., enhancing manufacturing operations or improvingdecision processes). Currently, there are different types of high-performanceoptimization software (e.g., ILOG CPLEX and Gurobi) that support engineers andscientists in solving optimization problems using classical computers. In orderto utilize quantum resources, users require domain-specific knowledge ofquantum algorithms, SDKs and libraries, which can be a limiting factor for anypractitioner who wants to integrate this technology into their workflows. Ourgoal is to add software infrastructure to a classical optimization package sothat application developers can interface with quantum platforms readily whensetting up their workflows. This paper presents a tool for the seamlessutilization of quantum resources through a classical interface. Our approachconsists of a Python library extension that provides a backend to facilitateaccess to multiple quantum providers. Our pipeline enables optimizationsoftware developers to experiment with quantum resources selectively and assessperformance improvements of hybrid quantum-classical optimization solutions.

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