一种用于分析大型实验粉末X射线衍射数据的混合量子神经网络

中文摘要 AI 辅助

定量分析实验粉末X射线衍射数据在评估复杂多相材料和噪声测量时仍具挑战性。我们引入了一种混合量子神经网络框架，旨在无需迭代精修即可直接从一维粉末衍射图案中提取定量参数，如相质量分数和尺度因子。该模型结合了噪声感知的经典模拟器预训练与基于量子处理单元特征的快速下游微调，确保了对硬件退相干的稳定性。我们通过将训练好的网络部署到IBM量子计算机上，分析来自三相固体氧化物燃料电池（约含10,000个图案）和四相锂离子电池（约含20,000个图案）的实验X射线衍射计算机断层扫描数据集，展示了该方法的实际效用。该网络成功重建了定量空间相图，与经典Rietveld精修结果高度一致，为使用量子计算硬件分析真实世界材料表征数据铺平了道路。

英文摘要

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 practical utility of this approach by deploying the trained network onto an IBM quantum computer to analyse experimental X-ray diffraction computed tomography datasets from a three-phase solid oxide fuel cell containing ca. 10,000 patterns and a four-phase lithium-ion battery containing ca. 20,000 patterns). The network successfully reconstructs quantitative spatial phase maps in strong agreement with classical Rietveld refinement, paving the way for using quantum computing hardware to analyse real-world materials characterisation data.

发表机构

• Finden ltd（芬登有限公司）
• National Quantum Computing Centre（国家量子计算中心）
• University College London（伦敦大学学院）
• Research Complex at Harwell（哈威尔研究中心）
• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。