Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization

TL;DR

This paper explores the use of Variational Quantum Circuits in reinforcement learning for dynamic portfolio optimization, showing potential advantages over classical methods but highlighting current practical deployment challenges.

Contribution

It introduces a quantum reinforcement learning approach based on variational quantum circuits for portfolio optimization, demonstrating competitive performance with classical models.

Findings

Quantum agents achieve comparable or better risk-adjusted returns.

Quantum circuit execution is fast, but cloud deployment introduces latency.

QRL shows promise for complex, high-dimensional decision environments.

Abstract

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