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Published directly by arXiv.
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…
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| Age | -12.3 |
| Primary source | 20 |
| Total | 7.7 |
These components add up to 7.699999999999999, not 7.7. The total is what ordered your brief; the difference means a component was not recorded, so treat this breakdown as incomplete rather than as the whole reason.