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

TL;DR

The paper tackles whether quantum machine learning can offer practical advantages in AMM and DeFi trading. It conducts large-scale backtests across 10 models and three crypto assets, comparing classical ML, pure quantum ML, and quantum-classical hybrids, including transformer baselines. The results show hybrid quantum-classical approaches, especially QASA Sequence, delivering the highest returns and favorable risk metrics, while pure quantum models underperform and classical ensembles remain highly reliable. The study demonstrates that hybrid architectures can achieve strong profitability with manageable risk, suggesting a pragmatic path for incorporating quantum methods into DeFi trading workflows while highlighting current limitations of purely quantum approaches. These insights inform model selection, feature engineering, and future research directions in quantum-enhanced financial modeling.

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.

Figures (8)

• Figure 1: QASA model.
• Figure 2: Quantum RWKV architecture.
• Figure 3: The VQC used in Quantum RWKV.
• Figure 4: All models equity curve comparison.
• Figure 5: Risk return scatter of all models.