ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Virtualization as a New Scaling Law for Semiconductor Devices Beyond Geometric Scaling.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- FPGA-based emulation framework for two-qubit quantum computation: Design, implementation, and validation.PloS one · 2026Article
- Quantum AI in Speech Emotion Recognition.Entropy (Basel, Switzerland) · 2025Article
- Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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3 authors.
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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.
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