Quantum Computing Applications for High-Energy Physics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Quantum Electronics".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 2921

Special Issue Editors


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Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: quantum computing; quantum machine learning; high-energy physics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Interests: quantum computing; quantum machine learning; neuromorphic computing

Special Issue Information

Dear Colleagues,

Over the years, the high-energy physics (HEP) community has witnessed the coevolution of computing and fundamental science. Researchers in the fields of particle physics and computing have built upon each other’s successes. Particularly, computing plays an essential role in HEP. As computing grew increasingly more sophisticated, its progress enabled new scientific discoveries and breakthroughs.

Quantum computing (QC) has the potential to speed up some of the most computationally expensive tasks in HEP. Therefore, in recent years, there has been a proliferation of quantum algorithms applied to HEP data analysis and simulations. The recent development of QC platforms and simulators available for public experimentation led to a general influx in research on quantum algorithms and applications. In particular, quantum algorithms were recently proposed to tackle the computational challenges of particle physics data processing and analysis. Some of these examples include assigning detector hits or signals into tracks to reconstruct the path of the originating particle, signal and background interactions, and clustering particles into so-called "particle jets".

Furthermore, it is expected that quantum computers will be more successful in the modeling of quantum effects, a difficult or impossible task for classical devices. Physicists use computer models to predict the behavior of fundamental particles. One such example is the modeling of parton showers. Additionally, quantum machine learning methods were extensively used in HEP.

This Special Issue will include any aspects related to quantum computing applications to HEP, such as simulations, data analysis, and quantum machine learning.

Dr. Andrea Delgado
Dr. Kathleen Hamilton
Guest Editors

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Keywords

  • high-energy physics
  • particle physics
  • quantum computing
  • quantum machine learning

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Published Papers (1 paper)

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Research

18 pages, 2301 KiB  
Article
Prediction of the Judd–Ofelt Parameters of Dy3+-Doped Lead Borosilicate Using Artificial Neural Network
by Amel A. Alhussan, Mohamed S. Gaafar, Mafawez Alharbi, Samir Y. Marzouk, Sayer Alharbi, Hussain ElRashidy, Mai S. Mabrouk, Hussah N. AlEisa and Nagwan Abdel Samee
Electronics 2022, 11(7), 1045; https://doi.org/10.3390/electronics11071045 - 26 Mar 2022
Cited by 8 | Viewed by 2334
Abstract
Developments in the field of glass research necessitate the mimicking of the optical properties of glass materials before melting the raw materials, as they are very expensive nowadays. An artificial neural network (ANN) was utilized during this work to train and predict the [...] Read more.
Developments in the field of glass research necessitate the mimicking of the optical properties of glass materials before melting the raw materials, as they are very expensive nowadays. An artificial neural network (ANN) was utilized during this work to train and predict the Judd–Ofelt parameters of various glasses, such as Ω2, Ω4 and Ω6, and the radiative lifetimes of many different types of rare-earth-doped glasses. The optimized ANN architecture for forecasting the Judd–Ofelt parameters were found to be very near to the experimentally measured parameters. Then, the conferred ANN model was employed to predict the Judd–Ofelt parameters of some newly prepared borosilicate glasses. Therein, a new glass system of 0.25 PbO–0.2 SiO2–(0.55 − x) B2O3–x Dy2O3, was prepared in order to employ the melt-quenching technique. The parameter results of the Judd–Ofelt theory, as well as the Ω2, Ω4 and Ω6 and radiative lifetimes showed that the supplementation of Dy2O3 switched the BO4 units to BO3 units with oxygens that were non-bridging atoms, thus weakening the glass frameworks. Therefore, it is very important to use an ANN to predict the Judd–Ofelt parameters of several rare-earth-doped glasses as luminescent materials. Full article
(This article belongs to the Special Issue Quantum Computing Applications for High-Energy Physics)
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