Quantum Feature Amplification Network

Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model

Announce Type: replace-cross Abstract: Direct-register quantum generative models for calorimeter shower simulation tie the quantum output dimension to the image dimension, so the required register size grows with the full image. Recent quantum-assisted methods reduce this pressure only by moving part of the generative task into hybrid latent-variable models. Consequently, current quantum demonstrations remain far below detector-scale geometries used in high-energy physics.