量子神经网络提升风电预测精度，最高达93%
Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations
- 设计12种量子神经网络结构，结合两种特征映射与六种纠缠策略
- 使用4个输入参数时，量子模型预测准确率达93%，优于经典方法
- 为量子机器学习在实际能源场景中的应用提供实证支持
量子机器学习（QML）是量子计算与机器学习交叉的新兴领域，旨在利用量子叠加和纠缠等原理增强经典机器学习。然而，由于当前噪声中等规模量子（NISQ）设备的限制，其实际优势仍存疑。本研究系统评估了量子神经网络（QNN），即类人工神经网络的量子实现，证明其在预测任务中优于经典方法。我们构建并测试了12种不同配置的QNN，采用两种独特的量子特征映射与六种纠缠策略设计量子线路（ansatz）。在风电数据集上的实验表明，使用Z特征映射的QNN在仅依赖4个输入参数的情况下，风力发电输出预测准确率最高可达93%。结果表明，QNN在预测性能上超越经典模型，凸显了量子机器学习在真实应用场景中的潜力。
原文摘要 · Abstract (English)
Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning, aiming to enhance classical machine learning methods by leveraging quantum mechanics principles such as entanglement and superposition. However, skepticism persists regarding the practical advantages of QML, mainly due to the current limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns by extensively assessing Quantum Neural Networks (QNNs)-quantum-inspired counterparts of Artificial Neural Networks (ANNs), demonstrating their effectiveness compared to classical methods. We systematically construct and evaluate twelve distinct QNN configurations, utilizing two unique quantum feature maps combined with six different entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset reveal that QNNs employing the Z feature map achieve up to 93% prediction accuracy when forecasting wind power output using only four input parameters. Our findings show that QNNs outperform classical methods in predictive tasks, underscoring the potential of QML in real-world applications.
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