Paper: arXiv 2510.15903

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

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 9.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper contains advanced mathematical formalisms, including quantum circuit equations and statistical formulas for feature engineering, while also presenting extensive backtesting results with specific performance metrics across multiple assets.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>QML vs CML in AMM DeFi Trading"] --> B["Data: Multi-Asset Backtesting<br/>10 Models, Crypto Assets"]
  B --> C["Computational Processes<br/>Classical ML, QML, Hybrid Models"]
  C --> D{"Backtesting & Evaluation<br/>Returns & Sharpe Ratio"}
  D --> E["Key Findings<br/>Hybrid QML: 11.2% Avg Return<br/>Best: QASA Sequence (13.99%)<br/>CML: 9.8% Avg Return"]

Cited References (25)

  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. Linear-layer-enhanced quantum long short-term memory for carbon price forecasting (2023)
  5. Learning to program variational quantum circuits with fast weights (2024)
  6. Long short-term memory (1997)
  7. Attention is all you need (2017)
  8. A multi-source dataset of urban life in the city of Milan and the Province of Trentino (2015)
and 17 more
  1. Long Short-Term Memory (1997) — link
  2. Quantum Machine Learning (2017) — link
  3. RWKV: Reinventing RNNs for the Transformer Era (2023)
  4. Informer: Beyond efficient transformer for long sequence time-series forecasting (2021)
  5. Quantum long short-term memory (QLSTM) vs. classical LSTM in time series forecasting: a comparative study in solar power forecasting (2024)
  6. Differential privacy (2006)
  7. Quantum circuit learning (2018)
  8. R 'e nyi differential privacy (2017)
  9. Quantum machine learning for quantum anomaly detection (2018)
  10. Dynamic differential-privacy preserving sgd (2021) — link
  11. Learning differentially private recurrent language models (2017) — link
  12. Differentially private federated tensor completion for cloud–edge collaborative AIoT data prediction (2023)
  13. Quantum machine learning with differential privacy (2023)
  14. Differential privacy in quantum computation (2017)
  15. Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market — → 2506.20930
  16. Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance — → 2508.02685
  17. Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market — → 2506.20930

3 of 25 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.