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

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.