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Collider events on a quantum computer

Gustafson, Gösta LU ; Prestel, Stefan LU ; Spannowsky, Michael and Williams, Simon (2022) In Journal of High Energy Physics 2022(11).
Abstract

High-quality simulated data is crucial for particle physics discoveries. Therefore, parton shower algorithms are a major building block of the data synthesis in event generator programs. However, the core algorithms used to generate parton showers have barely changed since the 1980s. With quantum computers’ rapid and continuous development, dedicated algorithms are required to exploit the potential that quantum computers provide to address problems in high-energy physics. This paper presents a novel approach to synthesising parton showers using the Discrete QCD method. The algorithm benefits from an elegant quantum walk implementation which can be embedded into the classical toolchain. We use the ibm_algiers device to sample parton... (More)

High-quality simulated data is crucial for particle physics discoveries. Therefore, parton shower algorithms are a major building block of the data synthesis in event generator programs. However, the core algorithms used to generate parton showers have barely changed since the 1980s. With quantum computers’ rapid and continuous development, dedicated algorithms are required to exploit the potential that quantum computers provide to address problems in high-energy physics. This paper presents a novel approach to synthesising parton showers using the Discrete QCD method. The algorithm benefits from an elegant quantum walk implementation which can be embedded into the classical toolchain. We use the ibm_algiers device to sample parton shower configurations and generate data that we compare against measurements taken at the ALEPH, DELPHI and OPAL experiments. This is the first time a Noisy Intermediate-Scale Quantum (NISQ) device has been used to simulate realistic high-energy particle collision events.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Jets and Jet Substructure, Parton Shower
in
Journal of High Energy Physics
volume
2022
issue
11
article number
35
publisher
Springer
external identifiers
  • scopus:85141531138
ISSN
1029-8479
DOI
10.1007/JHEP11(2022)035
language
English
LU publication?
yes
id
055e4d70-1863-443b-a522-e56a0845838a
date added to LUP
2022-12-02 10:46:38
date last changed
2024-04-18 08:59:51
@article{055e4d70-1863-443b-a522-e56a0845838a,
  abstract     = {{<p>High-quality simulated data is crucial for particle physics discoveries. Therefore, parton shower algorithms are a major building block of the data synthesis in event generator programs. However, the core algorithms used to generate parton showers have barely changed since the 1980s. With quantum computers’ rapid and continuous development, dedicated algorithms are required to exploit the potential that quantum computers provide to address problems in high-energy physics. This paper presents a novel approach to synthesising parton showers using the Discrete QCD method. The algorithm benefits from an elegant quantum walk implementation which can be embedded into the classical toolchain. We use the ibm_algiers device to sample parton shower configurations and generate data that we compare against measurements taken at the ALEPH, DELPHI and OPAL experiments. This is the first time a Noisy Intermediate-Scale Quantum (NISQ) device has been used to simulate realistic high-energy particle collision events.</p>}},
  author       = {{Gustafson, Gösta and Prestel, Stefan and Spannowsky, Michael and Williams, Simon}},
  issn         = {{1029-8479}},
  keywords     = {{Jets and Jet Substructure; Parton Shower}},
  language     = {{eng}},
  number       = {{11}},
  publisher    = {{Springer}},
  series       = {{Journal of High Energy Physics}},
  title        = {{Collider events on a quantum computer}},
  url          = {{http://dx.doi.org/10.1007/JHEP11(2022)035}},
  doi          = {{10.1007/JHEP11(2022)035}},
  volume       = {{2022}},
  year         = {{2022}},
}