Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays (Adv. Sci. 15/2025)

Abstract

Quantum Machine Learning

In article number 2411573, Zeheng Wang, Timothy van der Laan, and Muhammad Usman introduce a quantum algorithm-driven framework to compress chemiresistive sensor data. By employing a self-adaptive quantum kernel (SAQK), classical data are mapped into a quantum state space, where quantum principal component analysis (qPCA) is used to achieve significant dimensionality reduction. This innovative method not only minimizes information loss but also improves the subsequent AI-driven readout accuracy. The work highlights the synergy of quantum algorithms and AI, paving the way for efficient IoT data handling on noisy intermediate-scale quantum (NISQ) devices.