# QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction

arXiv: https://arxiv.org/abs/2408.03088 · HTML: https://mlsanity.com/paper/2408.03088/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Symbolic Discovery of Optimization Algorithms](https://mlsanity.com/paper/2302.06675/) (2023)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Reinforcement Learning with Quantum Variational Circuits](https://mlsanity.com/paper/2008.07524/) (2020)
- [Data Quality in Imitation Learning](https://mlsanity.com/paper/2306.02437/) (2023)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [QSAN: A Near-term Achievable Quantum Self-Attention Network](https://mlsanity.com/paper/2207.07563/) (2022)
- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [Uncovering Instabilities in Variational-Quantum Deep Q-Networks](https://mlsanity.com/paper/2202.05195/) (2022)
- [A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks](https://mlsanity.com/paper/2212.02721/) (2022)
- [Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics](https://mlsanity.com/paper/2402.00776/) (2024)
- [Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving](https://mlsanity.com/paper/1907.06838/) (2019)
- [Reinforcement Learning with Expert Trajectory For Quantitative Trading](https://mlsanity.com/paper/2105.03844/) (2021)
- [MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading](https://mlsanity.com/paper/2407.01577/) (2024)
- [Modeling stock price dynamics on the Ghana Stock Exchange: A Geometric Brownian Motion approach](https://mlsanity.com/paper/2403.13192/) (2024)

## Built on by (most cited first, 9 of 9)

- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction](https://mlsanity.com/paper/2503.15403/) (2025)
- [LEP-QNN: Loan Eligibility Prediction using Quantum Neural Networks](https://mlsanity.com/paper/2412.03158/) (2024)
- [MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption](https://mlsanity.com/paper/2412.01858/) (2024)
- [AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling](https://mlsanity.com/paper/2409.01626/) (2024)
- [QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2503.08759/) (2025)
- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)
- [A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference](https://mlsanity.com/paper/2603.09789/) (2026)
- [Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation](https://mlsanity.com/paper/2605.20801/) (2026)

## Abstract (arXiv)

Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-classical hybrid algorithms to tackling real-world financial challenges. In this respect, we corroborate the concept with rigorous backtesting and validate the framework's performance under realistic market conditions, by including fixed transaction cost per trade. This paper introduces a Quantum Attention Deep Q-Network (QADQN) approach to address these challenges through quantum-enhanced reinforcement learning. Our QADQN architecture uses a variational quantum circuit inside a traditional deep Q-learning framework to take advantage of possible quantum advantages in decision-making. We gauge the QADQN agent's performance on historical data from major market indices, including the S&P 500. We evaluate the agent's learning process by examining its reward accumulation and the effectiveness of its experience replay mechanism. Our empirical results demonstrate the QADQN's superior performance, achieving better risk-adjusted returns with Sortino ratios of 1.28 and 1.19 for non-overlapping and overlapping test periods respectively, indicating effective downside risk management.