用自适应量子核方法提升化学电阻传感器阵列的数据压缩效率
Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays
- 引入自适应量子核主成分分析，动态优化数据降维过程
- 在低量子比特条件下仍优于传统主成分分析，保留更多关键信息
- 适合资源受限的物联网场景，尤其适用于传感器数据高效读出
物联网设备的快速发展带来了海量数据处理挑战。化学电阻传感器阵列（CSAs）虽易于制造，但因多传感器同步工作产生大量数据。传统主成分分析（cPCA）在降维过程中难以有效保留关键信息。本文提出自适应量子核主成分分析（SAQK PCA），作为更优的数据压缩方案。实验表明，在各类后端机器学习任务中，尤其是量子比特有限的低维场景下，SAQK PCA 显著优于 cPCA。该结果表明，尽管当前量子比特数量受限，噪声中等规模量子（NISQ）计算机仍有望通过提升 CSA 数据压缩与读出的效率和可靠性，推动物联网实际应用中的数据处理革新。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。