Task-aligned spectral inductive biases in parameterized quantum circuits
Parameterized quantum circuits are widely investigated in quantum machine learning, yet how their architecture can provide task-aligned inductive biases remains poorly understood. We study data reuploading quantum circuits with two distinct inputs: a signal that varies between samples and a fixed, task-defined spectral query that specifies how the signal should be probed. We show that encoding the query can change the circuit's hypothesis class over the signal by modulating how its existing signal frequencies are combined, without introducing new sample-dependent information. This mechanism matches tasks built on nonuniform Fourier transforms, whose evaluation frequencies specify the quer...
| Authors | Felix Frohnert, Adrián Pérez-Salinas, Sebastian Lange et al. |
|---|---|
| Primary category | quant-ph |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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