Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers

ArXiv ID: 2510.15903 “View on arXiv”

Authors: Chi-Sheng Chen, Aidan Hung-Wen Tsai

Abstract

This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.

Keywords: quantum machine learning, hybrid quantum-classical models, automated market makers, decentralized finance, cryptocurrency, Cryptocurrency

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail
  • Why: The paper contains advanced mathematical formalisms, including quantum circuit equations and statistical formulas for feature engineering, while also presenting extensive backtesting results with specific performance metrics across multiple assets.
  flowchart TD
    A["Research Goal<br/>QML vs CML in AMM DeFi Trading"] --> B["Data: Multi-Asset Backtesting<br/>10 Models, Crypto Assets"]
    B --> C["Computational Processes<br/>Classical ML, QML, Hybrid Models"]
    C --> D{"Backtesting & Evaluation<br/>Returns & Sharpe Ratio"}
    D --> E["Key Findings<br/>Hybrid QML: 11.2% Avg Return<br/>Best: QASA Sequence (13.99%)<br/>CML: 9.8% Avg Return"]