用量子信息概念增强经典神经网络，提升对撞机数据建模能力
QINNs: Quantum-Informed Neural Networks
- 将粒子视为量子比特，用量子费舍尔信息矩阵编码关联
- 在喷注分类任务中提升模型表达力与可塑性，准确率显著提高
- 结果可解释且适配现有深度学习框架，适合高能物理分析
经典深度神经网络能够学习对撞机数据中的复杂多粒子关联，但其归纳偏置很少基于物理结构。我们提出量子引导神经网络（QINNs），一种将量子信息概念与量子可观测量引入纯经典模型的通用框架。本文以具体实现为例：将每个粒子编码为一个量子比特，并使用量子费舍尔信息矩阵（QFIM）作为粒子关联的紧凑、基无关摘要。以喷注分类为例，QFIM作为轻量级嵌入用于图神经网络，提升了模型的表达能力和适应性。QFIM揭示了强子型顶夸克喷注与胶子喷注在物理上预期的不同模式。因此，QINNs为量子引导分析（如层析成像）提供了实用、可解释且可扩展的路径，特别增强了成熟的深度学习方法。
原文摘要 · Abstract (English)
Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, in this paper, we study one concrete realisation that encodes each particle as a qubit and uses the Quantum Fisher Information Matrix (QFIM) as a compact, basis-independent summary of particle correlations. Using jet tagging as a case study, QFIMs act as lightweight embeddings in graph neural networks, increasing model expressivity and plasticity. The QFIM reveals distinct patterns for QCD and hadronic top jets that align with physical expectations. Thus, QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses, that is, tomography, of particle collisions, particularly by enhancing well-established deep learning approaches.
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