Quantum-Enhanced Forecasting for Deep Reinforcement Trading

题目： 面向深度强化交易的量子增强预测。
作者： J. H. Chen 等（2025）。
摘要要点： 论文将量子启发的神经网络模块（如 QLSTM）与改造的深度强化学习算法（量子化的 A3C 变体）结合，用于短期趋势预测和策略优化。作者在外汇与加密货币上实现了概念性交易代理，观察到预测性能的温和提升与探索行为模式的变化，表明将量子计算思想引入金融 RL 是一条值得探索的新方向。
Quantum-Enhanced Forecasting for Deep Reinforcement Trading
Authors: J. H. Chen et al. — 2025.
Source / arXiv: arXiv:2509.09176 (Sep 2025).
English summary: This paper explores quantum-inspired neural network modules (e.g., QLSTM) combined with modified deep RL (Quantum A3C variant) to improve short-term trend prediction and policy optimization. The authors implement proof-of-concept trading agents (FX pairs and crypto) showing modest predictive gains and altered exploration patterns attributed to quantum-inspired representations—suggesting a new research direction at the intersection of quantum computing ideas and financial RL.