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

We formulate automated market maker (AMM) \emph{rebalancing} as a binary detection problem and study a hybrid quantum–classical self-attention block, $\mathbf{\text{Quantum}}\mathbf{\text{Adaptive}}\mathbf{\text{Self-Attention}}\mathbf{\text{(QASA)}}$. 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 $\mathbf{\text{BTCUSDC}}$ over $\mathbf{\text{Jan-2024–Jan-2025}}$ 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 $\mathbf{\text{QASA-Sequence}}$ variant attains the \emph{best single-model risk-adjusted performance} ($\mathbf{\text{13.99\%}}$ return; $\mathbf{\text{Sharpe}}\mathbf{\text{1.76}}$), while hybrid models average $\mathbf{\text{11.2\%}}$ return (vs.\ 9.8% classical; 4.4% pure quantum), indicating a favorable performance–stability–cost trade-off.