Automated Quantum Software and AI Engineering

2026-04-21

AI Summary

Quantum software and artificial intelligence development face significant challenges, including a scarcity of skilled personnel, low development efficiency, and complex deployment decisions in hybrid systems. This study presents the first systematic literature review on automated software engineering and AI methods specifically tailored for quantum and hybrid quantum-classical systems. It synthesizes existing techniques, tools, and application strategies, while identifying key automation approaches and critical research gaps. By addressing the lack of comprehensive reviews in this interdisciplinary domain, the work establishes a theoretical foundation and offers practical pathways to enhance the development efficiency and deployment intelligence of quantum–AI integrated systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

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 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.

Problem

• Quantum Software Engineering
• Quantum Artificial Intelligence
• Automation
• Hybrid Quantum-Classical Applications
• Quantum Computing

Innovation

• Quantum Software Engineering
• Quantum Artificial Intelligence
• Automation
• Hybrid Quantum-Classical Systems
• Systematic Literature Review