biostudies-literatureUnknown12(15)Wang ZThe 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.Advanced science (Weinheim, Baden-Wurttemberg, Germany)e2411573https://www.ebi.ac.uk/biostudies/studies/S-EPMC12005759biostudies-literatureSelf-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays.PMC12005759Usman Mvan der Laan TWang ZfalseSelf-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays.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.2025-01-01T00:00:00Z2025 Apr2025-07-08T03:12:48.801Z2025-07-08T03:12:48.801ZS-EPMC120057593985405710.1002/advs.202411573