mlsanityData to 7 Oct 2026

Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations

arXiv 2606.10448 ↗First posted 9 Jun 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

The financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes off-policy maximum-entropy methods like Soft Actor-Critic (SAC). Specifically, noisy state representations may produce unreliable Q-value estimates, and bootstrapping amplifies these errors, forming a failure mode we call the "Financial Entropy Trap". In this paper, we propose FPQC-SAC, an efficient and plug-and-play SAC variant that places a compact and bounded Parameterized Quantum Circuit (PQC) before the actor and critic networks to constrain feature propagation at the representation level, rather than filtering raw inputs or regularizing Q-values after bootstrapping. Notably, FPQC-SAC reduces the impact of extreme market fluctuations on Bellman target estimation, while trainable quantum entanglement preserves flexible cross-asset interactions. Empirical evaluations on real-world portfolio management tasks demonstrate that FPQC-SAC substantially enhances out-of-sample stability and cumulative returns by achieving a 66.89% relative gain in cumulative return over standard unconstrained SAC and outperforms the best continuous-control deep reinforcement learning baseline by approximately 27%. Open-source code is available at https://github.com/ZeyuLIU-UST/FPQC-SAC-main.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 9 Jun 2026.

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References · 29

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17 more
  1. 2021Dropout Q-Functions for Doubly Efficient Reinforcement Learningcited by 108
  2. 2019CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicitycited by 89
  3. 2020FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Financecited by 86
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  5. 2024Normalization and effective learning rates in reinforcement learningcited by 55
  6. 2024Quantum Architecture Search: A Surveycited by 31
  7. 2023Potential and limitations of random Fourier features for dequantizing quantum machine learningcited by 22
  8. 2022Quantum policy gradient algorithmscited by 15
  9. 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 14
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  11. 2022Variational Quantum Soft Actor-Critic for Robotic Arm Controlcited by 7
  12. 2025Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approachcited by 4
  13. 2021Robust and Scalable SDE Learning: A Functional Perspectivecited by 3
  14. 2025Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisionscited by 3
  15. 2026How to find expressible and trainable parameterized quantum circuits?cited by 2
  16. 2025Quantum deep reinforcement learning for humanoid robot navigation taskcited by 1
  17. 2026Hybrid TD3: Overestimation Bias Analysis and Stable Policy Optimization for Hybrid Action Spacecited by 1

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