Symmetry/Asymmetry in Quantum Computing and Quantum Machine Learning Algorithms for High Energy Physics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Physics".

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 2140

Special Issue Editors


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Guest Editor
Department of Physics, Loyola University Maryland, 4501 N. Charles Street, Baltimore, MD 21210-2699, USA
Interests: high energy physics; machine learning; electronics; simulation; grid computing; teaching; pedagogy; quantum machine learning
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Guest Editor
Computational Science Initiative, Brookhaven National Laboratory, New York, NY 11973-5000, USA
Interests: quantum computing; quantum machine learning; quantum optimal control; quantum error correction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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 of exploiting the capacity of the hardware for machine learning applications. An interesting question is whether we will be able to develop quantum algorithms that will be able to outperform those classical machine learning algorithms used by the HEP community for decades.

The High Energy Physics community used classical machine learning algorithms to address a wide variety of challenging problems, including searches for the Higgs boson and physics beyond the standard model. This Special Issue aims to gather the latest developments in quantum machine learning algorithms to address challenging problems in particle physics, such as particle classification, track and vertex reconstruction, and physics simulation, beyond the standard model searches and quantum entanglement.

Prof. Dr. Kamal Benslama
Dr. Samuel Yen-Chi Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • quantum mechanics
  • entanglement
  • machine learning
  • quantum computing
  • quantum algorithms
  • physics beyond the standard model
  • quantum information
  • particle physics
  • high energy physics
  • particle classification
  • collider
  • neutrino physics
  • dark matter
  • symmetries in quantum mechanics
  • symmetry in physics
  • asymmetry
  • Bell’s inequality

Published Papers (1 paper)

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Research

11 pages, 467 KiB  
Article
The Franson Experiment as an Example of Spontaneous Breaking of Time-Translation Symmetry
by David H. Oaknin
Symmetry 2022, 14(2), 380; https://doi.org/10.3390/sym14020380 - 14 Feb 2022
Cited by 3 | Viewed by 1905
Abstract
We describe an explicit statistical model of local hidden variables that reproduces the predictions of quantum mechanics for the ideal Franson experiment and sheds light on the physical mechanisms that might be involved in the actual experiment. The crux of our model is [...] Read more.
We describe an explicit statistical model of local hidden variables that reproduces the predictions of quantum mechanics for the ideal Franson experiment and sheds light on the physical mechanisms that might be involved in the actual experiment. The crux of our model is the spontaneous breaking of time-translation gauge symmetry by the hidden configurations of the pairs of photons locked in time and energy involved in the experiment, which acquire a non-zero geometric phase through certain cyclic transformations. Full article
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