Quantum Machine Learning in High Energy Physics

Abstract

Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.

arXiv comment25 pages, 9 figures, submitted to Machine Learning: Science and Technology, Focus on Machine Learning for Fundamental Physics collection

References in corpus (47)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Quantum Computing in the NISQ era and beyond
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• Quantum Simulation
• Supervised learning with quantum enhanced feature spaces
• Quantum Annealing in the Transverse Ising Model
• The theory of variational hybrid quantum-classical algorithms
• Quantum support vector machine for big data classification
• Quantum Circuit Learning
• Quantum Chemistry in the Age of Quantum Computing
• Quantum Convolutional Neural Networks
• Quantum principal component analysis
• Error mitigation for short-depth quantum circuits
• Evaluating analytic gradients on quantum hardware
• Real-time dynamics of lattice gauge theories with a few-qubit quantum computer
• Quantum Search by Local Adiabatic Evolution
• Quantum generative adversarial learning
• Quantum machine learning: a classical perspective
• Quantum generative adversarial networks
• Deep Learning and its Application to LHC Physics
• Cloud Quantum Computing of an Atomic Nucleus
• A generative modeling approach for benchmarking and training shallow quantum circuits
• Quantum Boltzmann Machine
• Hierarchical quantum classifiers
• Differentiable Learning of Quantum Circuit Born Machine
• SU(2) non-Abelian gauge field theory in one dimension on digital quantum computers
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Feedback-optimized parallel tempering Monte Carlo
• Learning the quantum algorithm for state overlap
• Implementing a distance-based classifier with a quantum interference circuit
• Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
• Neural-Network Quantum States, String-Bond States, and Chiral Topological States
• Quantum machine learning for quantum anomaly detection
• Simulations of Subatomic Many-Body Physics on a Quantum Frequency Processor
• Quantum adiabatic machine learning
• Variational Quantum Fidelity Estimation
• Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
• Linear Response on a Quantum Computer
• Quantum Computing for Neutrino-nucleus Scattering
• Quantum algorithms and lower bounds for convex optimization
• Quantum learning of coherent states
• Unsupervised classification of quantum data
• Quantum Associative Memory in HEP Track Pattern Recognition
• Unfolding as Quantum Annealing

Cited by in corpus (43)

• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface
• Quantum Machine Learning: from physics to software engineering
• Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• Quantum machine learning of large datasets using randomized measurements
• Style-based quantum generative adversarial networks for Monte Carlo events
• 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
• Large-scale quantum approximate optimization on non-planar graphs with machine learning noise mitigation
• Quantum Machine Learning for $b$-jet charge identification
• Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
• Qu8its for Quantum Simulations of Lattice Quantum Chromodynamics
• Let Quantum Neural Networks Choose Their Own Frequencies
• Quantum speedup for track reconstruction in particle accelerators
• Quantum Convolutional Neural Networks are Effectively Classically Simulable
• Quantum Generative Adversarial Networks For Anomaly Detection In High Energy Physics
• Loop-Free Tensor Networks for High-Energy Physics
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging
• 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
• A quantum algorithm for track reconstruction in the LHCb vertex detector
• Variational quantum computing for quantum simulation: principles, implementations, and challenges
• Detect anomalous quartic gauge couplings at muon colliders with quantum kernel k-means
• Quantum kernel learning Model constructed with small data
• Optimize the event selection strategy to study the anomalous quartic gauge couplings at muon colliders using the support vector machine and quantum support vector machine
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays
• Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer
• Unsupervised and lightly supervised learning in particle physics
• Quantum simulation of the phase transition of the massive Thirring model
• Quantum Edge Detection
• 1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
• Fourier Analysis of Variational Quantum Circuits for Supervised Learning
• Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection
• Quantum clustering and jet reconstruction at the LHC
• Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Learning Reduced Representations for Quantum Classifiers
• A quantum machine learning classifier to search for new physics
• Quantum Machine Learning with SQUID
• Digit quantum simulation of a fermion field in an expanding universe