Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
On the map: Deep reinforcement learning methods → Quantum reinforcement learning
Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 73 references that are in the map. First posted on arXiv 20 Jan 2026.
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- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2020Variational Quantum Algorithmscited by 692
- 2018Supervised learning with quantum enhanced feature spacescited by 665
- 2018Barren plateaus in quantum neural network training landscapescited by 630
- 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
- 2019Parameterized quantum circuits as machine learning modelscited by 332
- 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
- 2021Parametrized quantum policies for reinforcement learningcited by 90
- 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
- 2019Deep Reinforcement Learning for Tradingcited by 73
- 2022A Survey on Quantum Reinforcement Learningcited by 53
- 2020Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networkscited by 22
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- 2023Quantum Deep Hedgingcited by 22
- 2015Solving the Optimal Trading Trajectory Problem Using a Quantum Annealercited by 12
- 2020Portfolio Optimisation Using the D-Wave Quantum Annealercited by 10
- 2025Quantum Portfolio Optimization: An Extensive Benchmarkcited by 1
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