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📄 Abstract
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.
Authors (6)
Aritra Bal
Markus Klute
Benedikt Maier
Melik Oughton
Eric Pezone
Michael Spannowsky
Submitted
October 20, 2025
Key Contributions
Introduces Quantum-Informed Neural Networks (QINNs) by integrating quantum information concepts, specifically the Quantum Fisher Information Matrix (QFIM), into classical GNNs. QFIM acts as a lightweight, basis-independent embedding for particle correlations, enhancing model expressivity and interpretability for tasks like jet tagging in particle physics.
Business Value
Enhances the precision and interpretability of data analysis in fundamental scientific research, potentially accelerating discoveries in fields like particle physics.