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

arXiv: https://arxiv.org/abs/2409.00115 · HTML: https://mlsanity.com/paper/2409.00115/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Towards quantum enhanced adversarial robustness in machine learning](https://mlsanity.com/paper/2306.12688/) (2023)
- [Blue and Green-Mode Energy-Efficient Nanoparticle-Based Chemiresistive Sensor Array Realized by Rapid Ensemble Learning](https://mlsanity.com/paper/2403.01642/) (2024)

## Built on by (most cited first, 2 of 2)

- [Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy](https://mlsanity.com/paper/2511.00406/) (2025)
- [Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact](https://mlsanity.com/paper/2409.10803/) (2024)

## Abstract (arXiv)

The rapid growth of Internet of Things (IoT) devices necessitates efficient data compression techniques to handle the vast amounts of data generated by these devices. Chemiresistive sensor arrays (CSAs), a simple-to-fabricate but crucial component in IoT systems, generate large volumes of data due to their simultaneous multi-sensor operations. Classical principal component analysis (cPCA) methods, a common solution to the data compression challenge, face limitations in preserving critical information during dimensionality reduction. In this study, we present self-adaptive quantum kernel (SAQK) PCA as a superior alternative to enhance information retention. Our findings demonstrate that SAQK PCA outperforms cPCA in various back-end machine-learning tasks, especially in low-dimensional scenarios where access to quantum bits is limited. These results highlight the potential of noisy intermediate-scale quantum (NISQ) computers to revolutionize data processing in real-world IoT applications by improving the efficiency and reliability of CSA data compression and readout, despite the current constraints on qubit availability.