# Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading

arXiv: https://arxiv.org/abs/2509.09176 · HTML: https://mlsanity.com/paper/2509.09176/

## Key facts

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

## 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, 11 of 11)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Learning based Recommender System: A Survey and New Perspectives](https://mlsanity.com/paper/1707.07435/) (2017)
- [Quantum Long Short-Term Memory](https://mlsanity.com/paper/2009.01783/) (2020)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum-enhanced machine learning](https://mlsanity.com/paper/1610.08251/) (2016)
- [Asynchronous training of quantum reinforcement learning](https://mlsanity.com/paper/2301.05096/) (2023)
- [Forecasting directional movements of stock prices for intraday trading using LSTM and random forests](https://mlsanity.com/paper/2004.10178/) (2020)
- [Distributed Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/2011.11012/) (2020)
- [Improving Deep Reinforcement Learning Agent Trading Performance in Forex using Auxiliary Task](https://mlsanity.com/paper/2411.01456/) (2024)

## Built on by (most cited first, 1 of 1)

- [QnRL: Quantum-Native Reinforcement Learning](https://mlsanity.com/paper/2606.08276/) (2026)

## 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.