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.

翻译：物联网设备的快速增长需要高效的数据压缩技术来处理这些设备产生的大量数据。化学电阻传感器阵列作为物联网系统中易于制造但至关重要的组件，由于其多传感器同时操作，会产生海量数据。经典主成分分析方法作为应对数据压缩挑战的常见解决方案，在降维过程中面临关键信息保留不足的局限。本研究提出自适应性量子核主成分分析方法作为增强信息保留的优化方案。研究结果表明，在各类后端机器学习任务中，特别是在量子比特访问受限的低维场景下，SAQK PCA的性能优于经典PCA。这些发现凸显了噪声中等规模量子计算机在现实物联网应用中的数据处理变革潜力——通过提升CSA数据压缩与读取的效率和可靠性，尽管当前量子比特资源仍存在限制。