Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity
arXiv:2412.12397
A systematic study of single-qubit quantum data re-uploading for calorimetric particle identification, examining circuit and training hyperparameters under NISQ-oriented resource constraints.
This work applies a compact single-qubit data re-uploading model to particle classification in calorimetric experiments. It evaluates feature mappings, circuit depth, rotation choices, input scaling, optimiser settings, and parameter budgets, with the aim of identifying configurations that remain practical for NISQ devices.