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

ArXiv ID: 2508.02685 “View on arXiv”

Authors: Chi-Sheng Chen, Aidan Hung-Wen Tsai

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.

Keywords: XGBoost, Quantum Neural Networks (QNN), DeFi Yield Forecasting, Liquidity Allocation, Model Benchmarking, Decentralized Finance (DeFi)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper integrates advanced machine learning models including quantum neural networks with specific circuit architectures and hybrid feature maps, indicating moderate-to-high mathematical sophistication. It demonstrates strong empirical rigor through a detailed benchmarking study using real DeFi data (Curve Finance pools), clear train/test splits, feature engineering, and standard evaluation metrics (MAE, RMSE, directional accuracy).

  flowchart TD
    A["Research Goal<br>Benchmark classical vs. quantum models<br>for DeFi yield prediction on Curve Finance"] --> B["Data: 1 Year Historical Data<br>28 Curve Finance Pools"]
    B --> C["Methodology: Model Benchmarking<br>Train/Test Split & Evaluation Metrics"]
    C --> D{"Computational Process"}
    D --> E["Classical Models<br>XGBoost, Random Forest, LSTM, Transformer"]
    D --> F["Quantum Models<br>QNN, QSVM-QNN"]
    E & F --> G["Key Findings & Outcomes"]
    G --> H["Classical Ensemble Models Win<br>XGBoost MAE: 1.80 | Acc: 71.57%<br>Random Forest MAE: 1.77 | Acc: 71.36%"]
    G --> I["Quantum Models Underperform<br>Accuracy &lt; 50% | Higher Errors<br>Current limitations in DeFi application"]