A Hybrid Quantum Neural Network to Analyse Big Experimental Powder X-ray Diffraction Data

This paper presents a hybrid quantum neural network framework for quantitative analysis of experimental powder X-ray diffraction data. The model directly extracts physical parameters such as phase weight fractions and scale factors from 1D diffraction patterns without iterative refinement. A noise-aware classical simulator pre-training technique enhances the quantum network's performance. The approach addresses challenges in analyzing complex multiphase materials with noisy experimental measurements.