6 papers
Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance
Chi-Sheng Chen, Aidan Hung-Wen Tsai
We formulate automated market maker (AMM) \emph{rebalancing} as a binary detection problem and study a hybrid quantum--classical self-attention block, \textbf{Quantum Adaptive Self…
Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers
Chi-Sheng Chen, Aidan Hung-Wen Tsai
This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and…
Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance
Chi-Sheng Chen, Aidan Hung-Wen Tsai
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 ben…
Quantum Multimodal Contrastive Learning Framework
Chi-Sheng Chen, Aidan Hung-Wen Tsai, Sheng-Chieh Huang
In this paper, we propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. This groundbreak…
QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding
Chi-Sheng Chen, Samuel Yen-Chi Chen, Aidan Hung-Wen Tsai +1
Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEGNet…
Large Cognition Model: Towards Pretrained EEG Foundation Model
Chi-Sheng Chen, Ying-Jung Chen, Aidan Hung-Wen Tsai
Electroencephalography provides a non-invasive window into brain activity, offering valuable insights for neurological research, brain-computer interfaces, and clinical diagnostics…