Quantum Anomaly Detection for Collider Physics

Abstract

Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for relevant problems, there have been many claims of an empirical advantage with high energy physics datasets. These studies typically do not claim an exponential speedup in training, but instead usually focus on an improved performance with limited training data. We explore an analysis that is characterized by a low statistics dataset. In particular, we study an anomaly detection task in the four-lepton final state at the Large Hadron Collider that is limited by a small dataset. We explore the application of QML in a semi-supervised mode to look for new physics without specifying a particular signal model hypothesis. We find no evidence that QML provides any advantage over classical ML. It could be that a case where QML is superior to classical ML for collider physics will be established in the future, but for now, classical ML is a powerful tool that will continue to expand the science of the LHC and beyond.

18 pages, 6 figures v2: updated acknowledgment, fixed typos related to the output of VQC and QCL

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• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Stirring the false vacuum via interacting quantized bubbles on a 5564-qubit quantum annealer
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• Efficient Quantum Simulation of QCD Jets on the Light Front
• Quantum Computing Universal Thermalization Dynamics in a (2+1)D Lattice Gauge Theory
• Resonant Anomaly Detection with Multiple Reference Datasets
• Jet Discrimination with Quantum Complete Graph Neural Network
• Hilbert space fragmentation at the origin of disorder-free localization in the lattice Schwinger model
• Guided Quantum Compression for High Dimensional Data Classification
• Non-resonant Anomaly Detection with Background Extrapolation
• Quantum computation in fermionic thermal field theories
• Unsupervised and lightly supervised learning in particle physics
• Generative Invertible Quantum Neural Networks
• 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC