AI 中文总结
研究机器学习在量子计算和中微子物理中的应用，包括量子极限学习机框架及相关分析，还有深度学习用于水切伦科夫探测器图像分析，展现机器学习在基础物理复杂数据分析中的潜力与挑战。
AI 中文摘要
本论文研究机器学习方法在量子计算和中微子物理中的应用，尤其着重于为复杂高维数据构建有效表示。第一部分致力于量子极限学习机（QELMs），它是一种混合量子 - 经典框架，经典数据编码为量子态并经固定量子动力学处理，由经典读出层学习。在此框架内分析了编码策略、特征约简方法、哈密顿结构和测量的作用。第二部分涉及深度学习在中微子物理中对水切伦科夫探测器产生图像分析的应用。开发了包括残差网络在内的卷积架构用于现实模拟数据集中复杂事件分类，表明此类模型能有效从探测器数据提取相关信息。这些结果凸显了机器学习在基础物理复杂数据分析中的潜力，也概述了未来研究的相关挑战和方向。
英文摘要
This thesis investigates the application of machine-learning methods in the context of quantum computing and neutrino physics, with particular emphasis on the construction of effective representations for complex, high-dimensional data. The first part of the work is devoted to Quantum Extreme Learning Machines (QELMs), a hybrid quantum--classical framework in which classical data are encoded into quantum states and processed through fixed quantum dynamics, while learning is performed by a classical readout layer. Within this framework, we analyze the role of encoding strategies, feature-reduction methods, Hamiltonian structure, and measurement, with particular focus on the relationship between quantum dynamics, expressivity, entanglement, and classical simulability. The second part of the thesis concerns the application of deep learning to the analysis of images produced by water Cherenkov detectors in neutrino physics. Convolutional architectures, including residual networks, are developed for the classification of complex events in realistic simulated datasets, showing that such models can effectively extract relevant information from detector data. Taken together, these results highlight the potential of machine learning, in both its classical and quantum forms, as a powerful framework for the analysis of complex data in fundamental physics, while also outlining relevant challenges and directions for future research.
CommentsPhD Thesis
