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

The paper introduces FPQC-SAC, a novel variant of the Soft Actor-Critic (SAC) algorithm designed to mitigate bias in low-signal-to-noise ratio (SNR) financial environments by integrating a Parameterized Quantum Circuit (PQC) before the actor and critic networks. This architecture constrains feature propagation, effectively reducing the influence of market volatility on Bellman target estimation, and has demonstrated a 66.89% improvement in cumulative returns over standard SAC in empirical tests. This advancement is significant for practitioners as it offers a robust method to enhance stability and performance in financial reinforcement learning applications.