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.

翻译：随着量子计算技术的成熟，研究已逐步转向探索其应用领域。伴随人工智能的兴起，各类机器学习方法已被发展为量子电路与算法。其中，量子神经网络（QNNs）能够通过特征映射（FMs）将输入映射至量子电路，并借助变分模型调整参数值，从而适用于回归与分类任务。然而，针对特定应用问题设计合适的特征映射仍是一项重大挑战。鉴于此，本研究提出一种增强型量子神经网络（EQNN），其中包含本研究所设计的增强型特征映射（EFM）。该EFM能够将输入变量有效映射至更适用于量子计算的数值范围，作为变分模型的输入以提升精度。在实验环境中，本研究以移动数据流量预测为案例，根据用户移动数据使用情况推荐合适的资费套餐。所提出的EQNN与当前主流QNNs进行了对比，实验结果表明：EQNN在采用更少量子逻辑门的情况下实现了更高的准确率，且在不同优化算法下能更快收敛至最优解。