Unlocking Quantum Optimization

The major advances in quantum computing over the last few decades have sparked great interest in applying it to solve the most challenging computational problems in a wide variety of areas. One of the most pronounced domains here are optimization problems and a number of algorithmic approaches have been proposed for their solution. For the current noisy intermediate-scale quantum (NISQ) computers the quantum approximate optimization algorithm (QAOA), the variational quantum eigensolver (VQE), and quantum annealing (QA) are the central algorithms for this problem class. The two former can be executed on digital gate-model quantum computers, whereas the latter requires a quantum annealer. Across all hardware architectures and manufactures, the quantum computers available today share the property of being too error-prone to reliably execute involved quantum circuits as they typically arise from quantum optimization algorithms. In order to characterize the limits of existing quantum computers, many component and system level benchmarks have been proposed. However, owing to the complex nature of the errors in quantum systems these benchmark fail to provide predictive power beyond simple quantum circuits and small examples. Application oriented benchmarks have been proposed to remedy this problem, but both, results from real quantum systems as well as use cases beyond constructed academic examples, remain very rare. This paper addresses precisely this gap by considering two industrial relevant use cases: one in the realm of optimizing charging schedules for electric vehicles, the other concerned with the optimization of truck routes. Our central contribution are systematic series of examples derived from these uses cases that we execute on different processors of the gate-based quantum computers of IBM as well as on the quantum annealer of D-Wave.