Quantum Computing to Study Cloud Turbulence Properties
The analysis and investigation of the data obtained from Direct Numerical (DNS) simulation of droplet dynamics in cloud turbulence is a complex and time-consuming task when performaed on traditional computers. The DNS data generally have, a high spatial resolution <inline-formula> <tex-math...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10164085/ |
| _version_ | 1827893460834713600 |
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| author | Mukta Nivelkar Sunil Bhirud Manmeet Singh Rahul Ranjan Bipin Kumar |
| author_facet | Mukta Nivelkar Sunil Bhirud Manmeet Singh Rahul Ranjan Bipin Kumar |
| author_sort | Mukta Nivelkar |
| collection | DOAJ |
| description | The analysis and investigation of the data obtained from Direct Numerical (DNS) simulation of droplet dynamics in cloud turbulence is a complex and time-consuming task when performaed on traditional computers. The DNS data generally have, a high spatial resolution <inline-formula> <tex-math notation="LaTeX">$\approx 1mm$ </tex-math></inline-formula> and require considerable space to store. It is tedious to find specific features of this data, such as locating high and low vortex areas in cloud turbulence using machine learning algorithms. In this research, we employ quantum computing to examine and analyze cloud droplet dynamics data and present a quantum supervised machine learning algorithm, namely, a support vector machine (SVM) to segregate low and high vortex regions and investigate the droplet characteristics in those regions. The result show that use of quantum computers can accelerate the entire process, and quantum mechanics tools, such as quantum kernels and quantum circuits can better manage the complex nature of data than traditional methods. |
| first_indexed | 2024-03-12T21:54:17Z |
| format | Article |
| id | doaj.art-9079fd051b77480ba5ee794121afe139 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:54:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9079fd051b77480ba5ee794121afe1392023-07-25T23:00:28ZengIEEEIEEE Access2169-35362023-01-0111706797069010.1109/ACCESS.2023.328992410164085Quantum Computing to Study Cloud Turbulence PropertiesMukta Nivelkar0https://orcid.org/0000-0001-7452-6978Sunil Bhirud1Manmeet Singh2Rahul Ranjan3Bipin Kumar4https://orcid.org/0000-0001-7047-551XVeermata Jijabai Technological Institute, Mumbai, IndiaVeermata Jijabai Technological Institute, Mumbai, IndiaIndian Institute of Tropical Metereology, Pune, IndiaDepartment of Environmental Science, Stockholm University, Stockholm, SwedenIndian Institute of Tropical Metereology, Pune, IndiaThe analysis and investigation of the data obtained from Direct Numerical (DNS) simulation of droplet dynamics in cloud turbulence is a complex and time-consuming task when performaed on traditional computers. The DNS data generally have, a high spatial resolution <inline-formula> <tex-math notation="LaTeX">$\approx 1mm$ </tex-math></inline-formula> and require considerable space to store. It is tedious to find specific features of this data, such as locating high and low vortex areas in cloud turbulence using machine learning algorithms. In this research, we employ quantum computing to examine and analyze cloud droplet dynamics data and present a quantum supervised machine learning algorithm, namely, a support vector machine (SVM) to segregate low and high vortex regions and investigate the droplet characteristics in those regions. The result show that use of quantum computers can accelerate the entire process, and quantum mechanics tools, such as quantum kernels and quantum circuits can better manage the complex nature of data than traditional methods.https://ieeexplore.ieee.org/document/10164085/Quantum computingquantum machine learningDNScloud dropletvorticity |
| spellingShingle | Mukta Nivelkar Sunil Bhirud Manmeet Singh Rahul Ranjan Bipin Kumar Quantum Computing to Study Cloud Turbulence Properties IEEE Access Quantum computing quantum machine learning DNS cloud droplet vorticity |
| title | Quantum Computing to Study Cloud Turbulence Properties |
| title_full | Quantum Computing to Study Cloud Turbulence Properties |
| title_fullStr | Quantum Computing to Study Cloud Turbulence Properties |
| title_full_unstemmed | Quantum Computing to Study Cloud Turbulence Properties |
| title_short | Quantum Computing to Study Cloud Turbulence Properties |
| title_sort | quantum computing to study cloud turbulence properties |
| topic | Quantum computing quantum machine learning DNS cloud droplet vorticity |
| url | https://ieeexplore.ieee.org/document/10164085/ |
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