QINNs: Quantum-Informed Neural Networks

TL;DR

QINNs integrate quantum information concepts into neural networks, using quantum Fisher information matrices to improve particle collision analysis, offering enhanced interpretability and expressivity in collider data modeling.

Contribution

This paper introduces QINNs, a framework that incorporates quantum information measures into classical neural networks, demonstrated through jet tagging with improved expressivity and physical interpretability.

Findings

QFIM-based embeddings improve jet classification accuracy.

QINNs reveal physically meaningful patterns in collider data.

Enhanced model interpretability with quantum-inspired features.

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.