Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
arXiv:2103.03897 · doi:10.1007/JHEP08(2021)170
Abstract
Photonic Quantum Computers provides several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the complexity, with respect to the sample's feature-vector length, from to $\mathcal{O}(\mbox{log}(N))$. Due to the speed of the sampling algorithm and the feasibility of near-term photonic quantum devices, anomaly detection at the trigger level can become practical in future LHC runs.
arXiv comment24 pages, 7 figures
References in corpus (54)
- Scikit-learn: Machine Learning in Python
- Semi-Supervised Classification with Graph Convolutional Networks
- The anti-k_t jet clustering algorithm
- Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
- An Introduction to PYTHIA 8.2
- FastJet user manual
- Quantum information with continuous variables
- A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
- Quantum computation over continuous variables
- Better Jet Clustering Algorithms
- Quantum algorithms for supervised and unsupervised machine learning
- Gaussian Boson Sampling
- Continuous-variable quantum neural networks
- Jet substructure as a new Higgs search channel at the LHC
- Universal quantum logic in hot silicon qubits
- Strawberry Fields: A Software Platform for Photonic Quantum Computing
- PennyLane: Automatic differentiation of hybrid quantum-classical computations
- The SWAP test and the Hong-Ou-Mandel effect are equivalent
- Graph Neural Networks in Particle Physics
- Adversarially-trained autoencoders for robust unsupervised new physics searches
- Finding top quarks with shower deconstruction
- Gluon Field Digitization for Quantum Computers
- Simulation of Nonequilibrium Dynamics on a Quantum Computer
- Applications of Near-Term Photonic Quantum Computers: Software and Algorithms
- q-means: A quantum algorithm for unsupervised machine learning
- Parton Physics on a Quantum Computer
- Gaussian Boson Sampling for perfect matchings of arbitrary graphs
- Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
- BQP-completeness of Scattering in Scalar Quantum Field Theory
- A quantum hardware-induced graph kernel based on Gaussian Boson Sampling
- Fermion-Fermion Scattering in Quantum Field Theory with Superconducting Circuits
- Combining subjet algorithms to enhance ZH detection at the LHC
- Quantum Algorithms for Jet Clustering
- A robust anomaly finder based on autoencoders
- Sigma models on quantum computers
- Probing stop pair production at the LHC with graph neural networks
- Quantum Algorithms for Fermionic Quantum Field Theories
- Quantum machine learning for data scientists
- Unsupervised clustering for collider physics
- Quantum Computing for Quantum Tunnelling
- Search for the Elusive Higgs Boson Using Jet Structure at LHC
- The Dirac equation as a quantum walk over the honeycomb and triangular lattices
- A Simple Baseline Algorithm for Graph Classification
- Electromagnetic lattice gauge invariance in two-dimensional discrete-time quantum walks
- The fate of the false vacuum: Finite temperature, entropy and topological phase in quantum simulations of the early universe
- Unburied Higgs
- Suppressing Coherent Gauge Drift in Quantum Simulations
- Quantum Simulation of Field Theories Without State Preparation
- Dirac quantum walks on triangular and honeycomb lattices
- Observing the fate of the false vacuum with a quantum laboratory
- The Computational Complexity of Linear Optics
- Site-by-site quantum state preparation algorithm for preparing vacua of fermionic lattice field theories
- A Quantum Algorithm for Model-Independent Searches for New Physics
- A quantum walk with both a continuous-time and a continuous-spacetime limit
Cited by in corpus (6)
- Challenges for Unsupervised Anomaly Detection in Particle Physics
- Quantum walk approach to simulating parton showers
- Online-compatible Unsupervised Non-resonant Anomaly Detection
- Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
- Quantum Pathways for Charged Track Finding in High-Energy Collisions
- Preserving New Physics while Simultaneously Unfolding All Observables