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

TL;DR

This paper introduces a quantum-enhanced reinforcement learning framework with LSTM forecasting for financial trading, demonstrating improved performance and stability over classical methods in volatile markets.

Contribution

It presents a novel integration of quantum circuits and LSTM-based predictions into reinforcement learning for finance, showing enhanced results over traditional approaches.

Findings

Quantum models outperform classical in noisy conditions

LSTM predictions improve trading decision quality

Quantum-enhanced RL shows greater stability

Abstract

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.