# Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance

arXiv: https://arxiv.org/abs/2509.16955 · HTML: https://mlsanity.com/paper/2509.16955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Quantum Vision Transformers](https://mlsanity.com/paper/2209.08167/) (2022)
- [Quantum Self-Attention Neural Networks for Text Classification](https://mlsanity.com/paper/2205.05625/) (2022)
- [Quantum Adaptive Self-Attention for Quantum Transformer Models](https://mlsanity.com/paper/2504.05336/) (2025)
- [Improved Price Oracles: Constant Function Market Makers](https://mlsanity.com/paper/2003.10001/) (2020)
- [Strategic Liquidity Provision in Uniswap v3](https://mlsanity.com/paper/2106.12033/) (2021)
- [Benchmarking Classical and Quantum Models for DeFi Yield Prediction on Curve Finance](https://mlsanity.com/paper/2508.02685/) (2025)
- [Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures](https://mlsanity.com/paper/2508.21332/) (2025)
- [Replicating Market Makers](https://mlsanity.com/paper/2103.14769/) (2021)

## Built on by (most cited first, 2 of 2)

- [Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning](https://mlsanity.com/paper/2605.06734/) (2026)
- [Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study](https://mlsanity.com/paper/2512.06630/) (2025)

## Abstract (arXiv)

We formulate automated market maker (AMM) rebalancing as a binary detection problem and study a hybrid quantum--classical self-attention block, Quantum Adaptive Self-Attention (QASA). QASA constructs quantum queries/keys/values via variational quantum circuits (VQCs) and applies standard softmax attention over Pauli-Z expectation vectors, yielding a drop-in attention module for financial time-series decision making. Using daily data for BTCUSDC over Jan-2024--Jan-2025 with a 70/15/15 time-series split, we compare QASA against classical ensembles, a transformer, and pure quantum baselines under Return, Sharpe, and Max Drawdown. The QASA-Sequence variant attains the best single-model risk-adjusted performance (13.99% return; Sharpe 1.76), while hybrid models average 11.2% return (vs. 9.8% classical; 4.4% pure quantum), indicating a favorable performance--stability--cost trade-off.