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

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measurements. We introduce a hybrid quantum neural network framework designed to extract quantitative parameters, such as phase weight fractions and scale factors, directly from one-dimensional powder diffraction patterns without iterative refinement. The model combines noise-aware classical simulator pre-training with fast downstream fine-tuning on quantum processing unit features, ensuring stability against hardware decoherence. We demonstrate the pra

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