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European Safety and Reliability Conference (ESREL)

Exploring Kernel Based Quantum Machine Learning for Prognosis and Health Management Applications

Gabriel San Martín Silva, Enrique López Droguett

Published Jan 1, 2022• 0 citations
Publisher
DOI
10.3850/978-981-18-5183-4_s05-08-514-cd

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This paper showcases the application of quantum-based kernel methods in Prognosis and Health Management tasks for rotating machinery. We provide a concise yet complete introduction to quantum computing from a probabilistic perspective, describing how a quantum algorithm can be interpreted as a kernel function. Using this framework, we assess the efficacy of quantum kernels in two relevant machine learning domains: the categorization of unlabeled data through the application of a quantum-based clustering algorithm, and the reduction of high dimensional data through quantum-based kernel principal component analysis. These approaches are evaluated using a well-known benchmark dataset of ball-bearing elements. The dataset, originally in the form of time series data collected from accelerometer sensors, consists of three distinct classes, and is first processed to extract pertinent time features. Results indicate that the proposed quantum-based approach performs comparably to traditional methodologies, motivating future research efforts in the use of Quantum Kernels Functions for machine learning applications in the field of Prognosis and Health Management.
The healthcare industry is experiencing a significant digital transformation due to the proliferation of electronic health records, medical imaging technologies, wearable sensors, genomic sequencing, and Internet of Medical Things (IoMT) devices. These technologies produce enormous quantities of heterogeneous and high-dimensional data that hold valuable insights for disease diagnosis, prognosis, treatment planning and population health management. Conventional machine learning and deep learning methods have achieved significant success in extracting patterns from medical data, but their performance is increasingly limited by computational complexity, scalability limitations, and the increasing dimensionality of healthcare datasets. Quantum Machine Learning (QML) is a young interdisciplinary field, which integrates quantum computing with machine learning methods, and offers a promising way to overcome these challenges and enable more powerful intelligent healthcare systems.