Automated Quantum Software and AI Engineering
Nazanin Siavash, Armin Moin

TL;DR
This paper systematically reviews automated approaches in Quantum Software Engineering and Quantum AI, highlighting recent trends and future directions to enhance development and deployment of quantum-enabled systems.
Contribution
It uniquely focuses on automation in quantum and hybrid AI software engineering, identifying recent trends and future research directions.
Findings
Automation improves efficiency in quantum software development.
Hybrid quantum-classical applications benefit from automation in deployment decisions.
The review highlights emerging methods and future research directions in quantum AI automation.
Abstract
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…
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