对比量子与经典机器学习在DeFi交易中的表现，发现混合模型更优。
Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers
- 采用多类模型在多种加密资产上进行回测，比较量子与经典方法。
- 混合量子模型平均收益11.2%，夏普比1.42，优于经典模型的9.8%和1.47。
- QASA Sequence模型表现最佳，收益达13.99%，适合关注量子金融应用者。
本研究通过在多个加密货币资产上对10种模型进行大规模回测，全面比较了量子机器学习（QML）与经典机器学习（CML）在自动做市商（AMM）和去中心化金融（DeFi）交易策略中的表现。分析涵盖经典模型（随机森林、梯度提升、逻辑回归）、纯量子模型（VQE分类器、量子神经网络、量子支持向量机）、混合量子-经典模型（QASA Hybrid、QASA Sequence、QuantumRWKV）以及Transformer模型。结果表明，混合量子模型整体表现更优，平均收益达11.2%，平均夏普比为1.42；而经典模型平均收益为9.8%，平均夏普比为1.47。其中，QASA Sequence混合模型实现最高单个收益13.99%，夏普比达1.76，展现出量子-经典混合方法在AMM与DeFi交易策略中的潜力。
原文摘要 · Abstract (English)
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.
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