Unravelling physics beyond the standard model with classical and quantum anomaly detection

Abstract

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. Identifying signatures of new physics out of the enormous amount of data produced at the LHC falls into the class of anomaly detection and constitutes one of the greatest computational challenges. In this article, we propose a novel strategy to perform anomaly detection in a supervised learning setting, based on the artificial creation of anomalies through a random process. For the resulting supervised learning problem, we successfully apply classical and quantum Support Vector Classifiers (CSVC and QSVC respectively) to identify the artificial anomalies among the SM events. Even more promising, we find that employing an SVC trained to identify the artificial anomalies, it is possible to identify realistic BSM events with high accuracy. In parallel, we also explore the potential of quantum algorithms for improving the classification accuracy and provide plausible conditions for the best exploitation of this novel computational paradigm.

15 pages, 10 figures

References in corpus (10)

• PYTHIA 6.4 Physics and Manual
• An Introduction to PYTHIA 8.2
• Extending the Bump Hunt with Machine Learning
• Anomaly detection in high-energy physics using a quantum autoencoder
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Introduction to Machine Learning: Class Notes 67577
• Hybrid Quantum-Classical Graph Convolutional Network
• Numerical evidence against advantage with quantum fidelity kernels on classical data
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• How Much Structure Is Needed for Huge Quantum Speedups?

Cited by in corpus (20)

• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Machine Learning for Anomaly Detection in Particle Physics
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics
• Automatic and effective discovery of quantum kernels
• Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
• A Quantum Computing Implementation of Nuclear-Electronic Orbital (NEO) Theory: Towards an Exact pre-Born-Oppenheimer Formulation of Molecular Quantum Systems
• Jet Discrimination with Quantum Complete Graph Neural Network
• Quantum Simulation of Bound State Scattering
• Guided Quantum Compression for High Dimensional Data Classification
• Loop Feynman integration on a quantum computer
• Non-resonant Anomaly Detection with Background Extrapolation
• Unsupervised and lightly supervised learning in particle physics
• Generative Invertible Quantum Neural Networks
• Quantum integration of decay rates at second order in perturbation theory
• 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
• Simulating the Fermi-Hubbard model with long-range hopping on a quantum computer
• Learning Reduced Representations for Quantum Classifiers
• Quantum Chebyshev Probabilistic Models for Fragmentation Functions
• Graph theory inspired anomaly detection at the LHC