对比经典与量子模型在去中心化金融收益预测中的表现。
Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
- 用六种模型在28个Curve池子数据上做对比实验
- XGBoost和随机森林在方向准确率上达71.57%，误差最低1.77
- 量子模型表现差，方向准确率低于50%，不适用于当前DeFi时序数据
去中心化金融（DeFi）的兴起推动了对收益与绩效预测的高需求，以指导流动性分配策略。本研究在来自28个Curve Finance池子的一年历史数据上，对六种模型——XGBoost、随机森林、LSTM、Transformer、量子神经网络（QNN）以及基于量子特征映射的量子支持向量机（QSVM-QNN）——进行了基准测试。通过测试集上的平均绝对误差（MAE）、均方根误差（RMSE）和方向准确率评估性能。结果表明，经典集成模型（尤其是XGBoost和随机森林）显著优于深度学习和量子模型：XGBoost达到最高方向准确率71.57%，测试MAE为1.80；随机森林则取得最低测试MAE 1.77，方向准确率71.36%。相比之下，量子模型表现不佳，方向准确率低于50%，误差更高，凸显当前量子机器学习在真实世界DeFi时间序列数据中的局限性。该研究提供了可复现的基准和实用洞见，强调经典方法在该领域的稳健性优于新兴量子方法。
原文摘要 · Abstract (English)
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.
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