{"ID":2868078,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.16955","arxiv_id":"2509.16955","title":"Quantum Adaptive Self-Attention for Financial Rebalancing: An Empirical Study on Automated Market Makers in Decentralized Finance","abstract":"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-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 \\textbf{BTCUSDC} over \\textbf{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 \\textbf{QASA-Sequence} variant attains the \\emph{best single-model risk-adjusted performance} (\\textbf{13.99\\%} return; \\textbf{Sharpe 1.76}), while hybrid models average \\textbf{11.2\\%} return (vs.\\ 9.8\\% classical; 4.4\\% pure quantum), indicating a favorable performance--stability--cost trade-off.","short_abstract":"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-Attention (QASA)}. QASA constructs quantum queries/keys/values via variational quantum circuits (VQCs) and applies standard softmax attenti...","url_abs":"https://arxiv.org/abs/2509.16955","url_pdf":"https://arxiv.org/pdf/2509.16955v1","authors":"[\"Chi-Sheng Chen\",\"Aidan Hung-Wen Tsai\"]","published":"2025-09-21T07:27:14Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.LG\",\"q-fin.CP\"]","methods":"[\"Transformer\"]","has_code":false}