Quantum Artificial Intelligence for Software Engineering: the Road Ahead

TL;DR

This paper outlines a roadmap for applying Quantum AI to software engineering, highlighting potential benefits, current applications like test case optimization, and future challenges in leveraging quantum algorithms and machine learning.

Contribution

It introduces a comprehensive roadmap for integrating Quantum AI into software engineering, focusing on quantum optimization and machine learning approaches for various development phases.

Findings

Initial applications include test case optimization

Quantum algorithms can enhance software engineering tasks

Roadmap identifies key challenges for QAI adoption

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.