用量子机器学习预测南非水质量，量子支持向量机表现更优。
Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region
- 采用量子支持向量机与量子神经网络对比分析水质数据。
- 多项式与RBF核的量子支持向量机精度相同，均高于量子神经网络。
- 量子神经网络易陷入死神经问题，整体表现仍不及量子支持向量机。
本研究将量子机器学习技术应用于南非德班地区U20A流域的实际水质量预测。分别应用了量子支持向量分类器（QSVC）和量子神经网络（QNN），结果显示QSVC更易实现且准确率更高。对QSVC使用线性、多项式和径向基函数（RBF）三种核函数，发现多项式与RBF核性能完全一致。针对QNN，测试了不同优化器、学习率、电路噪声及权重初始化，但模型始终存在死神经问题。仅通过准确率与损失比较，发现使用Adam优化器时表现最佳，但仍低于最优的QSVC模型。
原文摘要 · Abstract (English)
In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum neural network (QNN), and we showed that the QSVC is easier to implement and yields a higher accuracy. The QSVC models were applied for three kernels: Linear, polynomial, and radial basis function (RBF), and it was shown that the polynomial and RBF kernels had exactly the same performance. The QNN model was applied using different optimizers, learning rates, noise on the circuit components, and weight initializations were considered, but the QNN persistently ran into the dead neuron problem. Thus, the QNN was compared only by accraucy and loss, and it was shown that with the Adam optimizer, the model has the best performance, however, still less than the QSVC.
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。