mlsanityData to 14 Oct 2026

Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading

arXiv 2509.09176 ↗First posted 11 Sep 2025cs.LG cs.CYcited by 1

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

The convergence of quantum-inspired neural networks and deep reinforcement learning offers a promising avenue for financial trading. We implemented a trading agent for USD/TWD by integrating Quantum Long Short-Term Memory (QLSTM) for short-term trend prediction with Quantum Asynchronous Advantage Actor-Critic (QA3C), a quantum-enhanced variant of the classical A3C. Trained on data from 2000-01-01 to 2025-04-30 (80% training, 20% testing), the long-only agent achieves 11.87% return over around 5 years with 0.92% max drawdown, outperforming several currency ETFs. We detail state design (QLSTM features and indicators), reward function for trend-following/risk control, and multi-core training. Results show hybrid models yield competitive FX trading performance. Implications include QLSTM's effectiveness for small-profit trades with tight risk and future enhancements. Key hyperparameters: QLSTM sequence length=4, QA3C workers=8. Limitations: classical quantum simulation and simplified strategy. footnoteThe views expressed in this article are those of the authors and do not represent the views of Wells Fargo. This article is for informational purposes only. Nothing contained in this article should be construed as investment advice. Wells Fargo makes no express or implied warranties and expressly disclaims all legal, tax, and accounting implications related to this article.

Key facts. Cited by 1 paper 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 11 of its 16 references that are in the map. First posted on arXiv 11 Sep 2025.

Builds on

11 of 16 references.

References · 11

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2016Quantum Machine Learningcited by 326
  3. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  4. 2017Deep Learning based Recommender System: A Survey and New Perspectivescited by 136
  5. 2020Quantum Long Short-Term Memorycited by 120
  6. 2017Quantum machine learning: a classical perspectivecited by 95
  7. 2016Quantum-enhanced machine learningcited by 79
  8. 2023Asynchronous training of quantum reinforcement learningcited by 20
  9. 2020Forecasting directional movements of stock prices for intraday trading using LSTM and random forestscited by 13
  10. 2020Distributed Deep Reinforcement Learning: An Overviewcited by 9
  11. 2024Improving Deep Reinforcement Learning Agent Trading Performance in Forex using Auxiliary Taskcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026QnRL: Quantum-Native Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
  2. 2026Variational Quantum Circuit-Based Reinforcement Learning for Dynamic Portfolio Optimization
  3. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction
  4. 2026Titans-QFWP: A Regime-Aware Hybrid Quantum Fast Weight Programmer for Portfolio Optimization
  5. 2025Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
  6. 2025Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study
  7. 2025QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
  8. 2026Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations