Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
This paper introduces a novel Quantum Reinforcement Learning (QRL) framework for dynamic portfolio optimization, leveraging Variational Quantum Circuits (VQCs) as function approximators within classical Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms. The approach is the first to apply QRL to sequential financial decision-making, moving beyond static portfolio optimization formulations. The quantum agents are trained via a hybrid quantum-classical loop, where the VQC encodes the policy or value function and parameters are updated using classical optimization. Empirical evaluation on real-world financial data demonstrates that the quantum agents achieve risk-adjusted performance comparable to, and in some cases exceeding, classical Deep RL models while using several orders of magnitude fewer parameters. This highlights the expressivity and efficiency of quantum circuits for representing complex decision policies. However, practical deployment on cloud-based quantum systems introduces substantial latency due to infrastructural overhead, making end-to-end runtime currently dominated by communication and queuing delays rather than quantum computation itself. The authors conclude that QRL is theoretically competitive and may become practically advantageous as quantum hardware and cloud infrastructure mature. The complete codebase is released as open source to facilitate further research and reproducibility.
Highlights
- 1First application of Variational Quantum Circuit-based Reinforcement Learning to dynamic portfolio optimization with real-world financial data.
- 2Quantum RL agents achieve risk-adjusted performance comparable to classical Deep RL models with several orders of magnitude fewer parameters.
- 3Demonstrates that quantum circuit execution is fast at hardware level, but cloud-based deployment introduces substantial latency limiting practical applicability.
- 4Open-source codebase released for reproducibility and further research.
Methods
- MVariational Quantum Circuits (VQC) as function approximators in Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms.
- MHybrid quantum-classical optimization loop: quantum circuit forward pass for action/value estimation, classical backpropagation for parameter updates.
- MEmpirical evaluation on real-world financial data with risk-adjusted performance metrics (Sharpe ratio, cumulative returns).
Results
- RQuantum RL agents achieve comparable or superior risk-adjusted returns relative to classical Deep RL baselines (DDPG, DQN) with significantly fewer trainable parameters.
- REnd-to-end runtime is dominated by cloud-based quantum system latency, making current practical deployment slower than classical alternatives.
- RQuantum circuit execution itself is inherently fast, suggesting potential advantage as deployment overheads decrease.
- RThe proposed QRL framework successfully learns adaptive trading policies in a dynamic portfolio optimization setting.
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