Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support
A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data. For matrix-valued inputs, relevant matrix-level relationships can be characterised through spectral values and spectral subspaces; however, common coordinate-wise rotation-gate data-encoding unitaries used in most quantum machine learning models do not explicitly construct such a matrix-level representation. We introduce Quantum Spectral Models (QSMs), in which we construct the generator of the data-encoding unitary directly from each input matrix. We study three QSM variants based on symmetric, global block, and non-overlapping patch...
| Authors | Peiyong Wang, Udaya Parampalli, Casey R. Myers |
|---|---|
| Primary category | quant-ph |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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