Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays
This work addresses data compression for IoT sensor arrays, offering a quantum-enhanced method that is incremental but shows potential for real-world applications.
The study tackled data compression for chemiresistive sensor arrays in IoT by proposing self-adaptive quantum kernel PCA, which outperformed classical PCA in machine-learning tasks, especially in low-dimensional scenarios with limited qubits.
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.