Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC

Abstract

The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, we propose a tensor network-based strategy for anomaly detection at the LHC and demonstrate its superior performance in identifying new phenomena compared to established quantum methods. Our model is a parametrized Matrix Product State with an isometric feature map, processing a latent representation of simulated LHC data generated by an autoencoder. Our results highlight the potential of tensor networks to enhance new-physics discovery.

References in corpus (41)

• A Large Mass Hierarchy from a Small Extra Dimension
• An Introduction to PYTHIA 8.2
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
• Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems
• Matrix Product Density Operators: Simulation of finite-T and dissipative systems
• Particle-flow reconstruction and global event description with the CMS detector
• Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems
• Tensor networks for complex quantum systems
• Simulating Lattice Gauge Theories within Quantum Technologies
• Lattice gauge theories simulations in the quantum information era
• Searching for New Physics with Deep Autoencoders
• Unsupervised Generative Modeling Using Matrix Product States
• Anomaly Detection for Resonant New Physics with Machine Learning
• Review on novel methods for lattice gauge theories
• Encoding of Matrix Product States into Quantum Circuits of One- and Two-Qubit Gates
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• Variational Autoencoders for New Physics Mining at the Large Hadron Collider
• Anomaly detection in high-energy physics using a quantum autoencoder
• Quantum Machine Learning in High Energy Physics
• Fractional quantum Hall effect in the interacting Hofstadter model via tensor networks
• Variational Autoencoders for Anomalous Jet Tagging
• Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
• Bump Hunting in Latent Space
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
• Machine Learning for Anomaly Detection in Particle Physics
• Efficient tensor network simulation of IBM's largest quantum processors
• Event Classification with Quantum Machine Learning in High-Energy Physics
• Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
• Autoencoders for Semivisible Jet Detection
• Quantum Anomaly Detection for Collider Physics
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at $\sqrt{s}=13$ TeV with the ATLAS detector
• Particle Track Reconstruction with Quantum Algorithms
• Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle $X$ in hadronic final states using $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector
• Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
• Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV
• Tensor networks for interpretable and efficient quantum-inspired machine learning
• Unsupervised Quantum Circuit Learning in High Energy Physics
• Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
• Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States