arXiv:2607. 11013v1 Announce Type: cross Abstract: 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.
By Spencer Topel
arXiv:2602. 23409v3 Announce Type: replace-cross Abstract: Angle-encoded variational quantum circuits admit a truncated Fourier series representation of their output, but approximating functions with maximum frequency $\omega_{\max}$ using fixed unary encoding requires $\mathcal{O}(\omega_{\max})$ encoding gates.
By Michael Poppel, Markus Baumann, Sebastian W\"olckert, Claudia Linnhoff-Popien, Jonas Stein
arXiv:2607. 16800v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, yet their training suffers from sensitivity to circuit depth, initialization, and landscape pathologies such as barren plateaus.
By Athanasios Hadjidimoulas, Tirthak Patel, Anastasios Kyrillidis
The paper presents a tutorial on enhancing quantum amplitude embedding through data re-uploading, a technique that repeatedly encodes input data between trainable blocks to increase model expressiveness. It demonstrates that each additional upload raises the reachable polynomial order by two and provides simulation results showing significant improvements in test coefficient of determination for higher-order targets. The study offers practical guidance on selecting the number of uploads based on the polynomial order required for a given task.
By Hyojun Ahn, Joongheon Kim
Amplitude embedding lets a variational quantum model read many classical features with few qubits, but the model output is then only a quadratic form in the normalized input. Data re-uploading, which...
arXiv:2604. 23743v2 Announce Type: replace-cross Abstract: Variational quantum circuits train poorly on chaotic forecasting, usually blamed on barren plateaus (exponentially vanishing gradients).
By Tushar Pandey