Structural Inductive Biases for Quantum Sequence Modeling on NISQ Hardware

Quantum sequence models combine architectural choices in information routing with the constraints of parameterized quantum circuits and near-term hardware, yet these effects are rarely compared under a common evaluation protocol. We empirically characterize quantum recurrent (QRNN), convolutional (QCNN), and projected-attention (QPA) models from controlled Boolean logic to constrained language modelling and physical IBM quantum processors. On Boolean tasks, multi-seed decision-geometry analysis reveals distinct but multi-axis architecture-dependent behaviour rather than a single smooth-versus-fragmented ordering. Controlled re-uploading ablations change both spectral structure and fit reliability, while a restricted single-query QPA admits a conditional additive decomposition that motivates the cumulative pooling used in the evaluated model. On the TALES language benchmark, classical baselines remain stronger on most generative metrics, and the relative ordering of the quantum architectures changes across tasks. Finally, we distinguish zero-shot execution of simulator-trained parameters from direct QPU optimisation. The two regimes need not agree: QRNN and QCNN both show measurable descent under multi-sample SPSA, while their hardware-training behaviour differs substantially despite comparatively modest zero-shot degradation. Overall, the results characterize architecture-dependent trade-offs in representation, optimisation, and physical execution rather than a uniform quantum advantage.