# Quantum-Enhanced Reinforcement Learning with LSTM Forecasting Signals for Optimizing Fintech Trading Decisions

arXiv: https://arxiv.org/abs/2507.12835 · HTML: https://mlsanity.com/paper/2507.12835/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [QnRL: Quantum-Native Reinforcement Learning](https://mlsanity.com/paper/2606.08276/) (2026)
- [Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations](https://mlsanity.com/paper/2606.10448/) (2026)
- [Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting](https://mlsanity.com/paper/2609.20594/) (2026)

## Abstract (arXiv)

Financial trading environments are characterized by high volatility, numerous macroeconomic signals, and dynamically shifting market regimes, where traditional reinforcement learning methods often fail to deliver breakthrough performance. In this study, we design a reinforcement learning framework tailored for financial systems by integrating quantum circuits. We compare (1) the performance of classical A3C versus quantum A3C algorithms, and (2) the impact of incorporating LSTM-based predictions of the following week's economic trends on learning outcomes. The experimental framework adopts a custom Gymnasium-compatible trading environment, simulating discrete trading actions and evaluating rewards based on portfolio feedback. Experimental results show that quantum models - especially when combined with predictive signals - demonstrate superior performance and stability under noisy financial conditions, even with shallow quantum circuit depth.