Quantum Computing Optimization: Application and Benchmark in Critical Infrastructure Assessment
Gabriel San Martín Silva, Enrique López Droguett · 2024
Over the last five years, quantum computing has attracted the attention of a wide variety of research fields with the promise of tackling challenges that currently are infeasible for traditional computing techniques. While quantum computing encompasses many different disciplines, such as quantum machine learning and quantum chemistry, recent advances in the subfield of quantum combinatorial optimization are especially interesting for the risk management and reliability optimization community. However, most of the literature on the topic presents abstract case studies based on problems of low complexity, making it difficult to truly assess the technology's current level of maturity. Thus, this paper presents a condensed review and benchmark of quantum combinatorial optimization, focusing the discussion on the Quantum Approximate Optimization Algorithm (QAOA), arguably one of the most promising quantum optimization approaches in the current literature. A case study regarding the identification of important nodes within the context of critical infrastructure is presented to assess the current state and limitations that the QAOA presents for the field. The case study is showcased in a simulated environment, reflecting ideal conditions assuming a perfect (noise-free) quantum computer. The results obtained indicate that a series of practical limitations and challenges arise when using quantum-based combinatorial optimization approaches in a non-abstract case study. A discussion of these limitations and some feasible solutions to explore in the future are presented at the end of the paper.