Using quantum annealing to generate test cases for cyber-physical systems

Abstract

Quantum computing offers the potential to address complex computational challenges, particularly in areas such as simulation and optimisation. Despite this promise, current gate-based quantum devices remain limited in scale and reliability, constraining their practical use. In this context, quantum annealing (QA) emerges as a viable and currently accessible alternative for solving optimisation problems, as it employs adiabatic quantum evolution to efficiently explore large and complex search spaces. This makes it a natural candidate for search-based software testing in the Cyber-Physical Systems (CPS) domain, which demands effective test cases due to their complexity and combination of continuous and discrete domains. This work explores the use of quantum annealing to enhance test case generation for CPS through a mutation-based approach. We employ quantum annealing to identify areas on test cases that can be improved and apply mutations to those areas. We encode test case mutation as a binary optimisation problem, and use quantum annealing to identify and target critical regions of the test cases for improvement. Our approach mechanises this process into an algorithm that uses D-Wave's quantum annealer to find the solution. As a main contribution, we offer insights into how quantum annealing can advance testing methodologies by empirically evaluating the correlation between problem size, hardware limitations, and the effectiveness of the results. Moreover, we compare the proposed method against state-of-the-art classical optimisation algorithms, targeting efficiency (time to generate test cases) and effectiveness (fault detection rates). Our results indicate that quantum annealing may enable faster test case generation (when problem decomposition, sampling and scheduling strategies are applied) while achieving comparable fault detection performance to state-of-the-art alternatives.

The authors' abstract, as published at the source. ACM Transactions on Software Engineering and Methodology, 2026 · DOI ↗