QINNs: Quantum-Informed Neural Networks

2025-10-20

AI Summary

Classical deep neural networks lack the capacity to incorporate physical priors inherent in high-energy physics processes. To address this, we propose the Quantum Information Neural Network (QINN) framework, which integrates the quantum Fisher information matrix (QFIM)—a compact, differentiable representation of inter-particle quantum correlations—into a graph neural network (GNN) architecture for physics-guided jet substructure modeling. This work constitutes the first application of QFIM in high-energy physics data analysis, enabling quantum-information-theoretic enhancement of classical models without requiring quantum hardware. On jet classification tasks, QINN significantly improves feature discriminability, uncovers jet differentiation patterns consistent with QCD expectations, and demonstrates strong generalization and scalability. By bridging quantum information theory and classical deep learning, QINN establishes a transferable paradigm for quantum-enhanced, physically interpretable machine learning in particle physics.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

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.

Problem

• Integrating quantum information concepts into classical neural network models
• Using Quantum Fisher Information Matrix to encode particle correlations
• Enhancing jet tagging interpretability through quantum-informed tomography approaches

Innovation

• Quantum Fisher Information Matrix encodes particle correlations
• QFIM embeddings enhance graph neural network expressivity
• Quantum-informed framework integrates quantum observables into classical models