Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays

• URL: http://arxiv.org/abs/2409.00115v2
• Date: Mon, 02 Dec 2024 10:25:47 GMT
• Title: Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays
• Authors: Zeheng Wang, Timothy van der Laan, Muhammad Usman,
• Abstract summary: We present self-adaptive quantum kernel (SAQK) PCA as a superior alternative to enhance information retention.<n>Results highlight the potential of noisy intermediate-scale quantum computers to revolutionize data processing in real-world IoT applications.
• Score: 0.6435156676256051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to handle the vast amounts of data generated by these devices. Chemiresistive sensor arrays (CSAs), a simple-to-fabricate but crucial component in IoT systems, generate large volumes of data due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA) methods, a common solution to the data compression challenge, face limitations in preserving critical information during dimensionality reduction. In this study, we present self-adaptive quantum kernel (SAQK) PCA as a superior alternative to enhance information retention. Our findings demonstrate that SAQK PCA outperforms cPCA in various back-end machine-learning tasks, especially in low-dimensional scenarios where access to quantum bits is limited. These results highlight the potential of noisy intermediate-scale quantum (NISQ) computers to revolutionize data processing in real-world IoT applications by improving the efficiency and reliability of CSA data compression and readout, despite the current constraints on qubit availability.

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