用量子神经网络提升股票市场预测准确率
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
- 设计专用量子电路，结合经典循环网络提取时间特征
- 两种混合优化策略均有效降低预测误差
- 适合关注量子计算在金融建模中应用的研究者
金融时序预测因复杂的时间依赖性和市场波动仍具挑战。本研究探索混合量子-经典方法在金融趋势预测中的潜力，利用量子资源增强特征表示与学习能力。提出一种专为金融场景设计的量子神经网络（QNN）回归器，采用新型变分电路（ansatz）。提出两种混合优化策略：(1) 顺序模式，先由经典循环模型（RNN/LSTM）提取时间依赖性，再进行量子处理；(2) 联合学习框架，同时优化经典与量子参数。通过时间序列交叉验证（TimeSeriesSplit）、k折交叉验证及预测误差分析，系统评估模型性能，验证了量子辅助学习在金融建模中的可行性，为量子资源在时序分析中的实际作用提供新见解。
原文摘要 · Abstract (English)
Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction by leveraging quantum resources for improved feature representation and learning. A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications. Two hybrid optimization strategies are proposed: (1) a sequential approach where classical recurrent models (RNN/LSTM) extract temporal dependencies before quantum processing, and (2) a joint learning framework that optimizes classical and quantum parameters simultaneously. Systematic evaluation using TimeSeriesSplit, k-fold cross-validation, and predictive error analysis highlights the ability of these hybrid models to integrate quantum computing into financial forecasting workflows. The findings demonstrate how quantum-assisted learning can contribute to financial modeling, offering insights into the practical role of quantum resources in time-series analysis.
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