Unlocking Quantum Optimization: A Use Case Study on NISQ Systems

TL;DR

This paper evaluates the practical performance of NISQ quantum computers on real-world optimization problems, demonstrating their capabilities and limitations through industrial use cases like electric vehicle charging and truck routing.

Contribution

It provides the first systematic application-oriented benchmarking of NISQ devices on real industrial optimization problems, highlighting their current practical utility.

Findings

Quantum computers can solve specific optimization problems with limited accuracy.

Error-prone nature of NISQ devices restricts their reliability for complex tasks.

Real-world use cases reveal the current capabilities and limitations of quantum hardware.

Abstract

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.