Overcoming Fourier Locking in Quantum Data Re-uploading Classifiers via Spectral Homotopy
Data re-uploading parameterized quantum circuits (DRU-PQCs) are universal function approximators, yet their expressivity produces oscillatory, non-convex loss landscapes that resist gradient-based optimization. We show that the primary optimization bottleneck in DRU-PQCs is not insufficient capacity but a structural failure mode we term Fourier locking (FL): because encoding weights and entangling layers are nonlinearly coupled, random initialization on high-frequency targets collapses the encoding parameters into spurious local minima. Two Fisher diagnostics characterize FL. The input-space quantum Fisher information $F_x$ measures the effective frequency content of the encoded state; th...
| Authors | Spencer Topel |
|---|---|
| Primary category | quant-ph |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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