Multi-View Hybrid Quantum Learning with Class-Conditional Source Alignment for Cross-Subject EEG

Cross-subject electroencephalography (EEG) learning combines few independent participants, heterogeneous task information, and distribution shift between people. Existing hybrid quantum EEG models compress all features before a single variational quantum circuit (VQC), which discards view-specific structure and leaves subject shift unaddressed. We introduce , a source-only architecture that preserves temporal, spatial, spectral, statistical, covariance, and task-dependent geometric views. Each view is processed by an independent shallow VQC and a lightweight residual encoder, a VQC-conditioned distribution weights both branches, and the fused quantum representation is regularized by class-conditional source-moment alignment and class-center separation. Under subject- or patient-disjoint evaluation on BNCI-P300, UCL ALS-EEG, and EEGET-ALS, obtains Macro-F1 scores of 86.50.5%, 66.80.5%, and 86.60.4%, exceeding the strongest of seven reimplemented architectures by 5.8, 2.0, and 11.0 points. Replacing each VQC by a LayerNorm–MLP matched to within 1% of the parameter count lowers Macro-F1 by 4.7, 3.1, and 3.0 points. Objective ablations identify conditional alignment as the largest individual contributor on BNCI and UCL, and the matched substitution isolates an additional encoder-specific increment within the aligned system. The circuits are analytically simulated bounded trigonometric feature maps; we claim no computational or hardware quantum advantage, and reported deviations quantify three-seed optimization variability under fixed outer folds.