Comparing Quantum and Classical Learning for DeFi Market-Making Strategies

Summary

This study compares quantum machine learning, classical machine learning, hybrid quantum-classical methods, and transformer models in automated market maker and decentralized finance trading strategies. It reports backtests across multiple cryptocurrency assets and ten model variants, including Random Forest, Gradient Boosting, Logistic Regression, several quantum classifiers, and hybrid sequence models. The stated aim is to assess whether quantum methods add value in this trading context.

The reported averages favor hybrid quantum approaches on return, while classical machine learning has a slightly higher average Sharpe ratio. A hybrid sequence model has the highest individual return and Sharpe ratio among the models described. These findings are backtest results, not evidence of live trading performance. The supplied description gives no information about data periods, transaction costs, model selection, baselines, or uncertainty estimates, all of which are needed to judge robustness and whether the reported differences would persist out of sample.

Key ideas

• The study backtests ten classical, quantum, hybrid, and transformer models for AMM and DeFi trading.
• The tested methods include standard supervised learning models and quantum classifiers.
• Hybrid quantum models have the higher reported average return, while classical models have a marginally higher average Sharpe ratio.
• A hybrid sequence model has the best reported individual return and Sharpe ratio.
• The summary does not describe costs, sample construction, or robustness checks, limiting interpretation of the backtest results.

Tags

# 2510.15903


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









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.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.