Published May 12, 2025
| Version v2
Preprint
Open
Quantum-Reinforcement-Learning-for-High-Frequency-Trading-QRL-HFT-: Q-TradeRL
Description
This project explores the application of quantum reinforcement learning (QRL) techniques to high-frequency trading (HFT) strategies. The theoretical foundation is backed by a detailed LaTeX report covering quantum noise, fidelity decay, and the feasibility of using quantum circuits in trading environments.
Key aspects:
Benchmarking on IBMQ backends Fidelity analysis under depolarizing noise Modular and reproducible structure Academic and research-ready format
Files
Q-TradeRL.pdf
Files
(135.3 kB)
| Name | Size | Download all |
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md5:109e27702f469defeda2fb1f169235f8
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135.3 kB | Preview Download |
Additional details
Related works
- Is supplement to
- Preprint: https://github.com/DonMask/Quantum-Reinforcement-Learning-for-High-Frequency-Trading-QRL-HFT-/tree/v1.1 (URL)