量子变量与量子内核机器学习模型对介电油的部分放电分类
José Miguel Monzón-Verona1,2, Santiago García-Alonso2,3, Francisco Jorge Santana-Martín1,2
1Electrical Engineering Department (DIE), University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究使用量子机器学习对矿物油中的电放电进行了分类. 量子变量和量子内核支持向量机器模型在现实数据上实现了高精度,展示了实际的量子计算应用.
科学领域:
- 电气工程 电气工程
- 量子计算是一种量子计算.
- 人工智能的人工智能
背景情况:
- 介电性矿物油中的电放电在电力系统中构成风险.
- 准确分类这些排放对于故障检测和系统可靠性至关重要.
- 现有的方法通常依赖于模拟,缺乏与真实世界数据的验证.
研究的目的:
- 使用人工智能 (AI) 与量子机器学习 (QML) 技术来分类电荷图像.
- 为了比较量子变量模型 (QVM) 和量子内核模型 (QKM) 的性能,用于排放类型的二进制分类.
- 评估QML在物理量子计算机上的实际应用,使用现实世界的数据.
主要方法:
- 从介电性矿物油中获得的电放电图像是使用高分辨率光学传感器捕获的.
- 使用Scikit图像的图像处理将功能减少到量子比特,用于量子电路训练.
- 两个QML模型,QVM和QKM (带量子内核的SVM),在Qiskit.中开发和训练.
- 模型是在三台容错物理量子IBM计算机上执行的.
主要成果:
- 在物理量子计算机上使用两个量子比特,QVM在一次放电组合中获得了92%的准确性,与经典模拟相比,其误差率为1%.
- QKM表明,增加量子比特数量显著提高了所有四种二进制组合的分类准确性.
- 该研究在现实数据上验证了QML模型,突出了其实际适用性.
结论:
- QML技术,特别是QVM和QKM,对于对介电性矿物油中的电放电进行分类是有效的.
- 物理量子计算机可以成功地执行QML算法,用于现实世界的工业应用.
- QKM的性能对量子比特数量敏感,更高的量子比特数量可以提高准确性.
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