Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support

Quantum machine learning models typically encode matrix-valued data using generic rotation gates that ignore matrix-level spectral structure. Quantum Spectral Models (QSMs) instead build the data-encoding unitary's generator directly from each input's spectral properties, testing symmetric, global-block, and patch-local Hamiltonian variants. Evaluated on Pendigits and synthetic spectral-statistics tasks, QSM variants achieved leading accuracy, with different variants excelling on different task types. This research has applications in advancing quantum machine learning architecture design, informing how structured, input-aware encodings can improve inductive bias, and offering broader principles for structure-aware model design applicable beyond quantum computing. Authors: Peiyong Wang, Udaya Parampalli, Casey R. Myers Paper: https://arxiv.org/abs/2607.22516v1