{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(15)"],"submitter":["Wang Z"],"pubmed_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."],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pagination":["e2411573"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12005759"],"repository":["biostudies-literature"],"pubmed_title":["Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays."],"pmcid":["PMC12005759"],"pubmed_authors":["Usman M","van der Laan T","Wang Z"],"additional_accession":[]},"is_claimable":false,"name":"Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays.","description":"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.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-08T03:12:48.801Z","creation":"2025-07-08T03:12:48.801Z"},"accession":"S-EPMC12005759","cross_references":{"pubmed":["39854057"],"doi":["10.1002/advs.202411573"]}}