Self-Adaptive Quantum Kernel Principal Component Analysis for Compact Readout of Chemiresistive Sensor Arrays

Area of Science:

• Quantum Computing
• Data Science
• Internet of Things

Background:

• Internet of Things (IoT) devices generate massive datasets, requiring efficient data compression.
• Chemiresistive sensor arrays (CSAs) are crucial in IoT but produce large data volumes.
• Classical principal component analysis (cPCA) faces limitations in preserving information in complex datasets.

Purpose of the Study:

• To introduce self-adaptive quantum kernel (SAQK) PCA as a method to improve information retention in CSA data.
• To evaluate SAQK PCA's performance against cPCA for IoT data compression.
• To explore the potential of noisy intermediate-scale quantum (NISQ) computers for real-world IoT applications.

Main Methods:

• Implementation of SAQK PCA for dimensionality reduction of CSA data.
• Comparative analysis of SAQK PCA and cPCA performance in machine learning tasks.
• Assessment of information preservation, particularly group structures, in low-dimensional data.

Main Results:

• SAQK PCA demonstrated superior performance over cPCA in machine learning tasks.
• The advantage of SAQK PCA was most pronounced in low-dimensional scenarios.
• SAQK PCA effectively preserved group structures within low-dimensional datasets, even with limited quantum bit resources.

Conclusions:

• SAQK PCA offers enhanced data compression for IoT applications utilizing CSAs.
• Noisy intermediate-scale quantum (NISQ) computers show promise for improving IoT data processing efficiency and reliability.
• Further development of quantum algorithms can address current qubit limitations for broader real-world impact.