Quantum Annealing for Jet Clustering with Thrust

Abstract

Quantum computing holds the promise of substantially speeding up computationally expensive tasks, such as solving optimization problems over a large number of elements. In high-energy collider physics, quantum-assisted algorithms might accelerate the clustering of particles into jets. In this study, we benchmark quantum annealing strategies for jet clustering based on optimizing a quantity called "thrust" in electron-positron collision events. We find that quantum annealing yields similar performance to exact classical approaches and classical heuristics, but only after tuning the annealing parameters. Without tuning, comparable performance can be obtained through a hybrid quantum/classical approach.

15 pages, 9 figures

References in corpus (4)

• PYTHIA 6.4 Physics and Manual
• A practical Seedless Infrared-Safe Cone jet algorithm
• Improving solutions by embedding larger subproblems in a D-Wave quantum annealer
• Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods

Cited by in corpus (9)

• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• Liouvillian Dynamics of the Open Schwinger Model: String Breaking and Kinetic Dissipation in a Thermal Medium
• Efficient Quantum Simulation of QCD Jets on the Light Front
• A Novel Quantum Realization of Jet Clustering in High-Energy Physics Experiments
• Loop Feynman integration on a quantum computer
• Quantum integration of decay rates at second order in perturbation theory
• Quantum jet clustering with LHC simulated data
• Quantum-annealing-inspired algorithms for multijet clustering
• Quantum Chebyshev Probabilistic Models for Fragmentation Functions