Quantum Reinforcement Learning Demonstrates Comparable Performance to Classical Deep RL in Portfolio Optimization

Global: Quantum Reinforcement Learning Demonstrates Comparable Performance to Classical Deep RL in Portfolio Optimization

Quantum Reinforcement Learning Framework

Researchers have introduced a quantum reinforcement learning (QRL) solution that employs variational quantum circuits to address the dynamic portfolio optimization problem. The study positions the QRL approach as a quantum analogue to established classical deep reinforcement learning methods, specifically the Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms.

Performance Evaluation

Using real-world financial datasets, the authors conducted an empirical evaluation that measured risk-adjusted returns. The results indicate that the quantum agents achieve performance levels comparable to, and in certain instances exceeding, those of the classical deep reinforcement learning models.

Parameter Efficiency and Robustness

The quantum models required several orders of magnitude fewer parameters than their classical counterparts, highlighting a notable improvement in parameter efficiency. Additionally, the quantum agents displayed reduced variability across differing market regimes, suggesting a degree of robustness under changing market conditions.

Deployment Challenges

Although quantum circuit execution is intrinsically rapid at the hardware level, the study notes that current cloud‑based quantum computing services introduce substantial latency. This infrastructural overhead presently dominates end‑to‑end runtime, limiting the immediate practical applicability of the QRL solution.

Future Prospects

The authors argue that as deployment overheads diminish, QRL could become practically advantageous, offering a promising paradigm for decision‑making in complex, high‑dimensional, and non‑stationary environments such as financial markets.

Open‑Source Release

The complete codebase supporting the research has been released as open source and is available on GitHub at https://github.com/VincentGurgul/qrl-dpo-public.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.