标题： 台湾股市行业轮动的量子强化学习交易代理 显示英文标题

标题： Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market

摘要： 我们提出了一种混合量子-经典强化学习框架，用于台湾股市的行业轮换。 我们的系统采用近端策略优化（PPO）作为核心算法，并将经典架构（LSTM、Transformer）和量子增强模型（QNN、QRWKV、QASA）作为策略和价值网络进行集成。 一个自动化的特征工程流程从资本份额数据中提取财务指标，以确保所有配置下的模型输入一致。 实证回测揭示了一个关键发现：尽管量子增强模型在训练奖励上始终表现更好，但在实际投资指标如累计收益和夏普比率方面却表现不如经典模型。 这种差异突显了在金融领域应用强化学习的核心挑战——即代理奖励信号与真实投资目标之间的不匹配。 我们的分析表明，当前的奖励设计可能鼓励对短期波动的过拟合，而不是优化风险调整后的收益。 这一问题在噪声中等规模量子（NISQ）约束下，由于量子电路的固有表达能力和优化不稳定性而变得更加严重。 我们讨论了这一奖励-性能差距的影响，并提出了未来改进的方向，包括奖励塑造、模型正则化和基于验证的早停策略。 我们的工作提供了一个可重复的基准和关于在现实金融中部署量子强化学习实际挑战的关键见解。 显示英文摘要

摘要： We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum-enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure consistent model input across all configurations. Empirical backtesting reveals a key finding: although quantum-enhanced models consistently achieve higher training rewards, they underperform classical models in real-world investment metrics such as cumulative return and Sharpe ratio. This discrepancy highlights a core challenge in applying reinforcement learning to financial domains -- namely, the mismatch between proxy reward signals and true investment objectives. Our analysis suggests that current reward designs may incentivize overfitting to short-term volatility rather than optimizing risk-adjusted returns. This issue is compounded by the inherent expressiveness and optimization instability of quantum circuits under Noisy Intermediate-Scale Quantum (NISQ) constraints. We discuss the implications of this reward-performance gap and propose directions for future improvement, including reward shaping, model regularization, and validation-based early stopping. Our work offers a reproducible benchmark and critical insights into the practical challenges of deploying quantum reinforcement learning in real-world finance.