The Cancer Registry of Norway (CRN) is a part of the Norwegian Institute of Public Health (NIPH) and is tasked with producing statistics on cancer among the Norwegian population. For this task, CRN develops, tests, and evolves a software system called Cancer Registration Support System (CaReSS). It is a complex socio-technical software system that interacts with many entities (e.g., hospitals, medical laboratories, and other patient registries) to achieve its task. For cost-effective testing of CaReSS, CRN has employed EvoMaster, an AI-based REST API testing tool combined with an integrated classical machine learning model. Within this context, we propose Qlinical to investigate the feasibility of using, inside EvoMaster, a Quantum Neural Network (QNN) classifier, i.e., a quantum machine learning model, instead of the existing classical machine learning model. Results indicate that Qlinical can achieve performance comparable to that of EvoClass. We further explore the effects of various QNN configurations on performance and offer recommendations for optimal QNN settings for future QNN developers.

翻译：挪威癌症登记处（CRN）隶属于挪威公共卫生研究所（NIPH），负责编制挪威人群的癌症统计数据。为此，CRN开发、测试并持续演进一个名为癌症登记支持系统（CaReSS）的软件系统。这是一个复杂的社会技术软件系统，需与众多实体（如医院、医学实验室及其他患者登记系统）交互以完成任务。为实现CaReSS的经济高效测试，CRN采用了基于人工智能的REST API测试工具EvoMaster，该工具集成了经典机器学习模型。在此背景下，我们提出Qlinical方案，旨在探究在EvoMaster内部使用量子神经网络（QNN）分类器（即量子机器学习模型）替代现有经典机器学习模型的可行性。实验结果表明，Qlinical能够达到与EvoClass相当的性能水平。我们进一步探究了不同QNN配置对性能的影响，并为未来QNN开发者提供了优化QNN参数设置的建议。