Quantum Artificial Intelligence for Software Engineering: the Road Ahead
Xinyi Wang, Shaukat Ali, Paolo Arcaini
TL;DR
This paper presents a research roadmap for applying quantum artificial intelligence (QAI) to software engineering, arguing that the growing complexity of modern software systems warrants quantum-enabled approaches. It synthesizes two main QAI strands—quantum optimization and quantum machine learning—and maps their potential utility to each software engineering phase (requirements, design, development, testing, maintenance). The authors enumerate concrete opportunities, such as using QNLP for ambiguity in requirements, QCNNs for design artifact analysis, and QA/QAOA for multi-objective optimization in architecture and test planning, while also detailing domain-specific challenges and general adoption hurdles like noise, qubit limits, and the need for empirical benchmarks. They advocate for a staged research program, including tooling, benchmarks, and empirical evaluations, to establish the feasibility and value of QAI in SE and to guide practical adoption. Overall, the work highlights the potential, practical constraints, and research directions necessary to leverage quantum computing for cost-effective, scalable software engineering outcomes.
Abstract
In order to handle the increasing complexity of software systems, Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within the field of software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as test case optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems cost-effectively with QAI. To this end, this paper presents a roadmap towards the application of QAI in software engineering. Specifically, we consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning. For each software engineering phase, we discuss how these QAI approaches can address some of the tasks associated with that phase. Moreover, we provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.