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.

翻译：金融时间序列预测因复杂的时间依赖性和市场波动而始终是一项具有挑战性的任务。本研究探索了混合量子-经典方法在金融趋势预测中的潜力，通过利用量子资源来改进特征表示和学习。我们引入了一种定制的量子神经网络回归器，其设计采用了一种专为金融应用定制的新颖参数化电路。提出了两种混合优化策略：（1）一种顺序方法，其中经典循环模型先提取时间依赖性，再进行量子处理；（2）一种联合学习框架，可同时优化经典参数和量子参数。通过使用TimeSeriesSplit、k折交叉验证和预测误差分析进行的系统评估，突显了这些混合模型将量子计算整合到金融预测工作流程中的能力。研究结果展示了量子辅助学习如何为金融建模做出贡献，并为量子资源在时间序列分析中的实际作用提供了见解。