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

• URL: http://arxiv.org/abs/2606.10448v1
• Date: Tue, 09 Jun 2026 05:55:06 GMT
• Title: Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
• Abstract summary: Financial market is a typical low signal-to-noise ratio (SNR) setting, which often destabilizes maximum-entropy methods like Soft Actor-Critic (SAC)<n>In this paper, we propose FPQC-SAC, an efficient and bounded riticized Quantum Circuit (PQC) before the actor and critic networks to constrain feature propagation at the representation level.<n> Empirical evaluations on real-world portfolio management tasks demonstrate that FPQC-SAC substantially enhances out-of-sample stability and cumulative returns.
• Score: 32.164152251693565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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