Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open APIs (e.g., Qiskit from IBM) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a hybrid quantum machine learning framework for health state diagnostics and prognostics. The framework is exemplified using a problem involving ball bearings dataset. To the best of our knowledge, this is the first attempt to harvest and leverage quantum computing to develop and apply a hybrid quantum-classical machine learning approach to a prognostics and health management (PHM) problem. We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
翻译:量子计算是一个新领域,最近吸引了来自广泛领域的研究人员,因为其代表力、灵活性和在速度和可伸缩性两方面都取得了有希望的结果。自2020年以来,全球实验室开始实验机器学习和量子计算之间的并列模型。量子处理器(QPUs)的可用性通过开放的API(例如IBM的Qiskit)向普通科学界提供,激发了对制定和测试新办法解决旧问题的兴趣。在本文中,我们提出了一个用于健康状况诊断和预测的混合量子机器学习框架。这个框架以一个包含球载数据集的问题为范例。就我们所知,这是首次尝试利用量子计算来开发和应用混合量子机学习方法来开发并解决预测性和健康管理问题。我们希望这份文件能启动在风险和可靠性领域探索和应用量子机器学习算法。