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

TL;DR

This paper introduces a self-adaptive quantum kernel PCA method that improves data compression for chemiresistive sensor arrays in IoT devices, leveraging NISQ quantum computers to outperform classical PCA especially in low-dimensional settings.

Contribution

The paper proposes a novel self-adaptive quantum kernel PCA technique that enhances information retention in sensor data compression, outperforming classical PCA methods in IoT applications.

Findings

SAQK PCA outperforms classical PCA in machine learning tasks.

Effective in low-dimensional scenarios with limited quantum resources.

Potential to revolutionize IoT data processing using NISQ computers.

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.