Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance

TL;DR

This paper benchmarks classical and quantum machine learning models for DeFi yield prediction, finding classical models like XGBoost outperform quantum models in accuracy and robustness on real-world data.

Contribution

It provides the first comprehensive benchmark comparing classical and quantum models for DeFi yield prediction using real data.

Findings

Classical ensemble models outperform quantum models in accuracy.

XGBoost achieves highest directional accuracy at 71.57%.

Quantum models underperform with accuracy below 50%.

Abstract

The rise of decentralized finance (DeFi) has created a growing demand for accurate yield and performance forecasting to guide liquidity allocation strategies. In this study, we benchmark six models, XGBoost, Random Forest, LSTM, Transformer, quantum neural networks (QNN), and quantum support vector machines with quantum feature maps (QSVM-QNN), on one year of historical data from 28 Curve Finance pools. We evaluate model performance on test MAE, RMSE, and directional accuracy. Our results show that classical ensemble models, particularly XGBoost and Random Forest, consistently outperform both deep learning and quantum models. XGBoost achieves the highest directional accuracy (71.57%) with a test MAE of 1.80, while Random Forest attains the lowest test MAE of 1.77 and 71.36% accuracy. In contrast, quantum models underperform with directional accuracy below 50% and higher errors, highlighting current limitations in applying quantum machine learning to real-world DeFi time series data. This work offers a reproducible benchmark and practical insights into model suitability for DeFi applications, emphasizing the robustness of classical methods over emerging quantum approaches in this domain.