提升量子神经网络精度，用新特征映射减少门数加快收敛
EQNN: Enhanced Quantum Neural Network
- 设计新型增强型特征映射（EFM），优化输入到量子态的编码方式
- 在移动数据预测任务中，准确率更高，量子门数更少，收敛更快
- 适合对效率与精度有要求的量子机器学习应用开发者
随着量子计算技术的成熟，研究逐渐转向其应用探索。结合人工智能发展，多种机器学习方法被转化为量子电路与算法。其中，量子神经网络（QNN）通过特征映射（FMs）将输入映射至量子电路，并利用变分模型调整参数，在回归与分类任务中具有应用潜力。然而，针对特定问题设计合适的特征映射仍是一大挑战。为此，本文提出增强型量子神经网络（EQNN），包含自主研发的增强型特征映射（EFM）。该EFM能将输入变量有效映射到更适合量子计算的值域，作为变分模型的输入以提升精度。实验以移动数据使用量预测为案例，基于用户数据推荐合适资费方案。结果表明，相较于当前主流QNN，EQNN在不同优化算法下均实现了更高准确率、更少量子逻辑门数，并更快收敛至最优解。
原文摘要 · Abstract (English)
With the maturation of quantum computing technology, research has gradually shifted towards exploring its applications. Alongside the rise of artificial intelligence, various machine learning methods have been developed into quantum circuits and algorithms. Among them, Quantum Neural Networks (QNNs) can map inputs to quantum circuits through Feature Maps (FMs) and adjust parameter values via variational models, making them applicable in regression and classification tasks. However, designing a FM that is suitable for a given application problem is a significant challenge. In light of this, this study proposes an Enhanced Quantum Neural Network (EQNN), which includes an Enhanced Feature Map (EFM) designed in this research. This EFM effectively maps input variables to a value range more suitable for quantum computing, serving as the input to the variational model to improve accuracy. In the experimental environment, this study uses mobile data usage prediction as a case study, recommending appropriate rate plans based on users' mobile data usage. The proposed EQNN is compared with current mainstream QNNs, and experimental results show that the EQNN achieves higher accuracy with fewer quantum logic gates and converges to the optimal solution faster under different optimization algorithms.
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