标题： 基于LSTM预测信号的量子增强强化学习在优化金融科技交易决策中的应用 显示英文标题

标题： Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions

摘要： 金融交易环境以高波动性、众多宏观经济信号和动态变化的市场制度为特征，在这种环境中，传统的强化学习方法往往无法实现突破性性能。 在本研究中，我们通过集成量子电路设计了一个针对金融系统的强化学习框架。 我们比较了（1）经典A3C与量子A3C算法的性能，以及（2）融入基于LSTM的下一周经济趋势预测对学习结果的影响。 实验框架采用了一个自定义的Gymnasium兼容的交易环境，模拟离散交易动作，并根据投资组合反馈评估奖励。 实验结果表明，在嘈杂的金融条件下，量子模型——尤其是当与预测信号结合时——表现出优越的性能和稳定性，即使量子电路深度较浅。 显示英文摘要

摘要： Financial trading environments are characterized by high volatility, numerous macroeconomic signals, and dynamically shifting market regimes, where traditional reinforcement learning methods often fail to deliver breakthrough performance. In this study, we design a reinforcement learning framework tailored for financial systems by integrating quantum circuits. We compare (1) the performance of classical A3C versus quantum A3C algorithms, and (2) the impact of incorporating LSTM-based predictions of the following week's economic trends on learning outcomes. The experimental framework adopts a custom Gymnasium-compatible trading environment, simulating discrete trading actions and evaluating rewards based on portfolio feedback. Experimental results show that quantum models - especially when combined with predictive signals - demonstrate superior performance and stability under noisy financial conditions, even with shallow quantum circuit depth.