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. QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses of particle collisions, particularly by enhancing well-established deep learning approaches.

We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics (HEP), where each particle is assigned to an individual qubit, enabling direct representation of collision events without classical compression. We demonstrate the effectiveness of 1P1Q in quantum machine learning (QML) through two applications: a Quantum Autoencoder (QAE) for unsupervised anomaly detection and a Variational Quantum Circuit (VQC) for supervised classification of top quark jets. The QAE successfully distinguishes signal jets from background QCD jets, achieving superior performance compared to a classical autoencoder while utilising significantly fewer trainable parameters. The VQC achieves competitive classification performance approaching state-of-the-art classical models despite minimal computational complexity. We additionally validate the QAE on real experimental data from the CMS detector, establishing the robustness of quantum algorithms in practical HEP applications.

This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8–6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. A collection of complementary anomaly detection methods — based on unsupervised, weakly supervised, and semisupervised algorithms — are used to maximise the sensitivity to unknown new physics signatures. These algorithms are applied to data corresponding to an integrated luminosity of 138 fb⁻¹ recorded by the CMS experiment at √s = 13 TeV. No significant excesses above background expectations are seen. The anomaly detection methods are found to significantly enhance the sensitivity to a variety of models relative to benchmark inclusive and substructure-based search strategies.

Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. We consider proton-proton collisions at the High-Luminosity LHC and demonstrate a successful knowledge transfer from an event-level graph neural network (GNN) to a particle-level small deep neural network (DNN). Our algorithm, DistillNet, is trained to predict whether a particle originates from the primary interaction vertex. The results show minimal loss during knowledge transfer to the student network while significantly improving computational resource requirements. This is demonstrated on a CPU and for a quantized and pruned student network deployed on an FPGA, proving the utility of this approach for fast AI in high-energy physics trigger stages.

We present the performance of machine learning-based anomaly detection techniques for extracting potential new physics phenomena in a model-agnostic way with the CMS experiment at the LHC. We introduce five distinct outlier detection or density estimation techniques — CWoLa, Tag N'Train, CATHODE, QUAK, and QR-VAE — tailored for the identification of anomalous jets originating from the decay of unknown heavy particles. We demonstrate the utility of these approaches in enhancing the sensitivity to a wide variety of potential signals and assess their comparative performance in simulation.