QINNs: Quantum-Informed Neural Networks
This work addresses the need for more physics-anchored inductive biases in deep learning for particle physics, offering an incremental improvement by integrating quantum observables into classical models.
The authors tackled the problem of classical neural networks lacking physics structure by proposing quantum-informed neural networks (QINNs), which incorporate quantum information concepts like the Quantum Fisher Information Matrix (QFIM) to encode particle correlations, resulting in enhanced model expressivity and interpretability for jet tagging tasks.
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.