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.

翻译：为应对软件系统日益增长的复杂性，人工智能已被应用于软件工程的多个领域，包括需求工程、编码、测试和调试。这促使"面向软件工程的人工智能"成为软件工程领域一个独立的研究方向。随着量子计算的发展，量子人工智能领域正在兴起，它能够提升经典人工智能的性能，并为解决传统软件工程问题展现出巨大潜力。量子人工智能在软件工程中的初步应用已经出现，例如测试用例优化。然而，前路依然广阔，为利用量子人工智能经济高效地解决复杂软件工程问题提供了丰富机遇。为此，本文提出了量子人工智能在软件工程中应用的路线图。具体而言，我们聚焦量子人工智能的两大主要类别——量子优化算法与量子机器学习。针对每个软件工程阶段，我们探讨了这些量子人工智能方法如何应对该阶段的相关任务。此外，我们还概述了为实现量子人工智能在软件工程中的成功应用所需应对的部分潜在挑战。