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

Paper: arXiv 2508.02685

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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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).

Research Flowchart

  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"]

Cited References (30)

1. Quantum adaptive self-attention for quantum transformer models (2025) — link
2. Quantum-enhanced channel mixing in rwkv models for time series forecasting (2025) — link
3. Quantum long short-term memory (2022)
4. Learning to program variational quantum circuits with fast weights (2024)
5. Long short-term memory (1997)
6. Attention is all you need (2017)
7. A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015)
8. Long Short-Term Memory (1997) — link

0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.