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

Abstract--W e 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 varia-tional 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. I. Introduction Decentralized finance (DeFi) has grown rapidly, with automated market makers (AMMs) becoming core primitives for on-chain liquidity provisioning [1]. AMMs implement constant-function designs (CFMMs) whose pricing and value properties have been formalized in recent theory, clarifying when pool prices are informative and how payoff replication arises [2], [3].