用量子电路实现强化学习，优化投资组合，表现媲美甚至超越经典模型。
Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
- 基于变分量子电路构建量子强化学习算法，模拟经典深度强化学习。
- 在真实金融数据上，量子模型风险调整后收益媲美或超过参数多几倍的经典模型。
- 适合对量子机器学习和金融决策感兴趣的科研与工程人员。
本文提出一种基于变分量子电路的量子强化学习（QRL）方法，用于动态投资组合优化。所实现的QRL方法是经典神经网络驱动的深度确定性策略梯度（DDPG）和深度Q网络（DQN）的量子对应版本。通过对真实金融数据的实证评估，我们发现量子智能体在风险调整后的表现可与、甚至在某些情况下超过参数量大几个数量级的经典深度强化学习模型。然而，尽管量子电路在硬件层面执行速度快，但在云量子系统上的实际部署引入了显著延迟，导致端到端运行时间主要受基础设施开销影响，限制了实际应用。总体而言，结果表明QRL在理论上与最先进的经典强化学习相当，并可能在部署开销降低后成为实用优势。这使QRL成为复杂、高维、非平稳环境（如金融市场）中动态决策的有前景范式。完整代码库已开源：https://github.com/VincentGurgul/qrl-dpo-public。
原文摘要 · Abstract (English)
This paper presents a Quantum Reinforcement Learning (QRL) solution to the dynamic portfolio optimization problem based on Variational Quantum Circuits. The implemented QRL approaches are quantum analogues of the classical neural-network-based Deep Deterministic Policy Gradient and Deep Q-Network algorithms. Through an empirical evaluation on real-world financial data, we show that our quantum agents achieve risk-adjusted performance comparable to, and in some cases exceeding, that of classical Deep RL models with several orders of magnitude more parameters. However, while quantum circuit execution is inherently fast at the hardware level, practical deployment on cloud-based quantum systems introduces substantial latency, making end-to-end runtime currently dominated by infrastructural overhead and limiting practical applicability. Taken together, our results suggest that QRL is theoretically competitive with state-of-the-art classical reinforcement learning and may become practically advantageous as deployment overheads diminish. This positions QRL as a promising paradigm for dynamic decision-making in complex, high-dimensional, and non-stationary environments such as financial markets. The complete codebase is released as open source at: https://github.com/VincentGurgul/qrl-dpo-public
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。