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This paper conducts a systematic literature review of (semi-) automated approaches in Quantum Software Engineering (QSE) and Quantum Artificial Intelligence (QAI), highlighting the efficiency gains achievable through automation in these fields. It emphasizes the critical need for such automation due to the limited pool of developers skilled in quantum computing and the complexities of hybrid quantum-classical applications. The findings reveal emerging trends and future directions that could significantly enhance productivity and efficiency for experts in quantum technologies.
Automation in quantum software engineering could dramatically elevate productivity, addressing the skills gap in a rapidly evolving field.
In this paper, we conduct a systematic literature review of (semi-) automated approaches to Quantum Software Engineering (QSE) and Quantum Artificial Intelligence (QAI). Prior work in the literature indicated that both Software Engineering (SE) and Artificial Intelligence (AI) practices may become more efficient by using (semi-) automated approaches. This also holds in the Quantum Computing (QC), Quantum Information Science (QIS), and Quantum Engineering (QE) world, as well as in hybrid quantum-classical applications. In fact, automation is even more crucial in such cases since there is a limited number of developers and AI experts (e.g., data scientists) who possess the required knowledge and skills in QC. Moreover, in hybrid setups, automation may help decide what part of the application should be deployed on quantum hardware and on which of the available quantum platforms, if applicable. This can be a significant help to achieve productivity leap and efficiency even for subject matter experts. Unlike prior literature reviews and surveys, this work focuses on automation in SE and AI for quantum and hybrid quantum-classical applications and identifies the recent trends and future directions through a systematic literature review. We are interested in methods and techniques that can enable a broader development and deployment of quantum and hybrid AI-enabled software systems.
