Self‐Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays (Adv. Sci. 15/2025)
Year: 2025
Type: article
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.
Source: Advanced Science
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,730
OpenAlex ID: W4409533505