Q-LEAP: Quantum-Inspired Localized Evolution and Anchor Projection for Cross-Subject sEMG Recognition

Cross-subject sEMG recognition is challenging due to substantial physiological variability, electrode shifts, and non-stationary signal drift across individuals. We propose Q-LEAP, a quantum-inspired framework that integrates localized evolution and anchor projection to learn transferable sEMG representations. Q-LEAP maps multichannel signals into normalized complex-valued states in Hilbert space, models cross-channel interactions through learnable localized evolution, and coherently fuses time- and frequency-domain representations via quantum-inspired interference. It further constructs fidelity-based anchor states and projects learned representations onto these anchors through projective measurement, yielding a stable reference space for cross-subject recognition. Extensive experiments on five NinaPro benchmarks demonstrate that Q-LEAP matches or exceeds twelve representative baselines in cross-session recognition, while providing substantial zero-shot and few-shot generalization advantages on unseen subjects and robust cross-database transfer between intact and amputee populations. These findings establish the effectiveness of quantum-inspired localized dynamics and fidelity geometry for robust physiological signal decoding under cross-subject variability.