A5052526505- Particle physics theoretical and experimental studies
- Quantum Chromodynamics and Particle Interactions
- High-Energy Particle Collisions Research
- Distributed and Parallel Computing Systems
- Particle Detector Development and Performance
- Particle Accelerators and Free-Electron Lasers
- Medical Imaging Techniques and Applications
- Scientific Computing and Data Management
- Black Holes and Theoretical Physics
- Dark Matter and Cosmic Phenomena
- Neutrino Physics Research
- Advanced Database Systems and Queries
- Superconducting Materials and Applications
- Particle accelerators and beam dynamics
- Radiation Detection and Scintillator Technologies
- Quantum Computing Algorithms and Architecture
- Quantum Mechanics and Applications
- Quantum Information and Cryptography
- Advanced Data Storage Technologies
- Pulsed Power Technology Applications
University of Bristol
2013-2023
Massachusetts Institute of Technology
2020
Moscow Institute of Thermal Technology
2019
Centro Brasileiro de Pesquisas Físicas
2013-2018
European Organization for Nuclear Research
2015
École Polytechnique Fédérale de Lausanne
2014
University of Manchester
2012
Identifying jets formed in high-energy particle collisions requires solving optimization problems over potentially large numbers of final-state particles. In this work, we consider the possibility using quantum computers to speed up jet clustering algorithms. Focusing on case electron-positron collisions, a well-known event shape called thrust whose optimum corresponds most jet-like separating plane among set particles, thereby defining two hemisphere jets. We show how formulate both as...
We explore the metric space of jets using public collider data from CMS experiment. Starting 2.3/fb 7 TeV proton-proton collisions collected at Large Hadron Collider in 2011, we isolate a sample 1,690,984 central with transverse momentum above 375 GeV. To validate performance detector reconstructing energy flow jets, compare Open Data to corresponding simulated samples for variety jet kinematic and substructure observables. Even without unfolding, find very good agreement track-based...
Many properties of Boolean functions can be tested far more efficiently than the function learned. However, this advantage often disappears when testers are limited to random samples--a natural setting for data science--rather queries. In work we investigate quantum version scenario: algorithms that test a $f$ solely from in form copies state $f$. For three well-established properties, show speedup lost restricting classical samples recovered by use data. monotonicity testing, give algorithm...