Anomaly detection in high-energy physics using a quantum autoencoder

Abstract

The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions. We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC. For a QCD $t\bar{t}$ background and resonant heavy Higgs signals, we find that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very efficiently. Moreover, this performance is reproducible on present quantum devices. This shows that quantum autoencoders are good candidates for analysing high-energy physics data in future LHC runs.

arXiv commentAdded three appendices and result for an additional benchmark scenario. Previous results are unchanged

References in corpus (41)

• Quantum Computing in the NISQ era and beyond
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• The anti-k_t jet clustering algorithm
• An Introduction to PYTHIA 8.2
• FastJet user manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• Quantum Simulation
• The total top quark pair production cross-section at hadron colliders through O(alpha_S^4)
• Quantum fingerprinting
• Parameterized quantum circuits as machine learning models
• Power of data in quantum machine learning
• Quantum autoencoders for efficient compression of quantum data
• Quantum Natural Gradient
• Robust data encodings for quantum classifiers
• Searching for New Physics with Deep Autoencoders
• Learning New Physics from a Machine
• Demonstration of quantum advantage in machine learning
• QCD or What?
• A generic anti-QCD jet tagger
• Novelty Detection Meets Collider Physics
• Adversarially-trained autoencoders for robust unsupervised new physics searches
• Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector
• A quantum algorithm for high energy physics simulations
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
• Uncovering latent jet substructure
• Autoencoders for unsupervised anomaly detection in high energy physics
• Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
• Anomaly detection with Convolutional Graph Neural Networks
• Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
• Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• Better Latent Spaces for Better Autoencoders
• Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
• Event Classification with Quantum Machine Learning in High-Energy Physics
• Challenges for Unsupervised Anomaly Detection in Particle Physics
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• Quantum Computing for Quantum Tunnelling
• Deep Set Auto Encoders for Anomaly Detection in Particle Physics
• Towards a Quantum Computing Algorithm for Helicity Amplitudes and Parton Showers
• Variational Quantum Anomaly Detection: Unsupervised mapping of phase diagrams on a physical quantum computer
• MUSiC: a model-unspecific search for new physics in proton-proton collisions at $\sqrt{s}=$ 13 TeV

Cited by in corpus (45)

• 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 phase detection generalisation from marginal quantum neural network models
• Quantum Anomaly Detection for Collider Physics
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Medium induced jet broadening in a quantum computer
• Completely Quantum Neural Networks
• Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
• Variational quantum one-class classifier
• Lorentz group equivariant autoencoders
• What's Anomalous in LHC Jets?
• Anomaly Detection under Coordinate Transformations
• Collider Events on a Quantum Computer
• Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Anomalies, Representations, and Self-Supervision
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
• Efficient Quantum Simulation of QCD Jets on the Light Front
• Jarzynski-like Equality of Nonequilibrium Information Production Based on Quantum Cross Entropy
• Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
• Accelerated quantum circuit Monte-Carlo simulation for heavy quark thermalization
• Quantum support vector data description for anomaly detection
• Jet Discrimination with Quantum Complete Graph Neural Network
• Quantum autoencoders for image classification
• Guided Quantum Compression for High Dimensional Data Classification
• Reconstructing charged particle track segments with a quantum-enhanced support vector machine
• Semi-visible jets, energy-based models, and self-supervision
• Non-resonant Anomaly Detection with Background Extrapolation
• Unsupervised and lightly supervised learning in particle physics
• ${\mathbb{Z}}_2\times {\mathbb{Z}}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
• Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates
• Generative Invertible Quantum Neural Networks
• Quantum Autoencoder for Multivariate Time Series Anomaly Detection
• Quantum integration of decay rates at second order in perturbation theory
• 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
• Quantum clustering and jet reconstruction at the LHC
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Quantum Patch-Based Autoencoder for Anomaly Segmentation
• Detecting New Physics as Novelty -- Complementarity Matters
• Guided Graph Compression for Quantum Graph Neural Networks
• Continuous-variable photonic quantum extreme learning machines for fast collider-data selection
• Single-Qudit Quantum Neural Networks for Multiclass Classification
• Applying Quantum Autoencoders for Time Series Anomaly Detection