Quantum Optimization Applications With Quark and Quapps: Bridging the Gap Between Application and Hardware [Focus: Quantum Software and its Engineering]

Solving discrete optimization problems is one of the most promising use cases on quantum computers with tremendous potential both in research and industry. We present two libraries quark and quapps which provide an easily usable, modular workflow to encode discrete optimization problems and interface them to both classical and quantum hardware. We provide a collection of already implemented optimization problems which can be easily adapted by the user and run on different solvers. From this we derive an additional instance data set that can directly be used to benchmark and test quantum hardware. In conjunction both libraries and the instance data set enable both expert and non-expert users to easily implement optimization problems, run them on classical and quantum machines and simplify benchmarking on quantum machines.