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

ABSTRACT: The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to manage the vast amounts of data they generate. Chemiresistive sensor arrays (CSAs), a simple yet essential component in IoT systems, produce large datasets due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA), a widely used solution for dimensionality reduction, often struggles to preserve critical information in complex datasets. In this study, the self-adaptive quantum kernel (SAQK) PCA is introduced as a complementary approach to enhance information retention. The results show that SAQK PCA outperforms cPCA in various back end machine-learning tasks, particularly in low-dimensional scenarios where quantum bit resources are constrained. Although the overall improvement is modest in some cases, SAQK PCA proves especially effective in preserving group structures within low-dimensional data. These findings underscore the potential of noisy intermediate-scale quantum (NISQ) computers to transform data processing in real-world IoT applications by improving the efficiency and reliability of CSA data compression and readout, despite current qubit limitations.

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12005759 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature