General implementation of quantum physics-informed neural networks
Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics. By integrating knowledge of physical laws and processes in Partial Differential Equations (PDEs), fast convergence and effective solutions...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Array |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005623000127 |
| _version_ | 1827940564003192832 |
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| author | Shashank Reddy Vadyala Sai Nethra Betgeri |
| author_facet | Shashank Reddy Vadyala Sai Nethra Betgeri |
| author_sort | Shashank Reddy Vadyala |
| collection | DOAJ |
| description | Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics. By integrating knowledge of physical laws and processes in Partial Differential Equations (PDEs), fast convergence and effective solutions are obtained. Since training modern Machine Learning (ML) systems is a computationally intensive endeavour, using Quantum Computing (QC) in the ML pipeline attracts increasing interest. Indeed, since several Quantum Machine Learning (QML) algorithms have already been implemented on present-day noisy intermediate-scale quantum devices, experts expect that ML on reliable, large-scale quantum computers will soon become a reality. However, after potential benefits from quantum speedup, QML may also entail reliability, trustworthiness, safety, and security risks. To solve the challenges of QML, we combine classical information processing, quantum manipulation, and processing with PINNs to accomplish a hybrid QML model named quantum based PINNs. |
| first_indexed | 2024-03-13T09:11:10Z |
| format | Article |
| id | doaj.art-9b6e1d26e811450d82344da889e40c17 |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-03-13T09:11:10Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-9b6e1d26e811450d82344da889e40c172023-05-27T04:26:28ZengElsevierArray2590-00562023-07-0118100287General implementation of quantum physics-informed neural networksShashank Reddy Vadyala0Sai Nethra Betgeri1Department of Computational Analysis and Modeling, Louisiana Tech University, Ruston, LA, United States; Corresponding author.Department of Computational Analysis and Modeling, Louisiana Tech University, Ruston, LA, United StatesRecently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics. By integrating knowledge of physical laws and processes in Partial Differential Equations (PDEs), fast convergence and effective solutions are obtained. Since training modern Machine Learning (ML) systems is a computationally intensive endeavour, using Quantum Computing (QC) in the ML pipeline attracts increasing interest. Indeed, since several Quantum Machine Learning (QML) algorithms have already been implemented on present-day noisy intermediate-scale quantum devices, experts expect that ML on reliable, large-scale quantum computers will soon become a reality. However, after potential benefits from quantum speedup, QML may also entail reliability, trustworthiness, safety, and security risks. To solve the challenges of QML, we combine classical information processing, quantum manipulation, and processing with PINNs to accomplish a hybrid QML model named quantum based PINNs.http://www.sciencedirect.com/science/article/pii/S2590005623000127PhysicsMachine learningQuantum computingPhysics informed neural network |
| spellingShingle | Shashank Reddy Vadyala Sai Nethra Betgeri General implementation of quantum physics-informed neural networks Array Physics Machine learning Quantum computing Physics informed neural network |
| title | General implementation of quantum physics-informed neural networks |
| title_full | General implementation of quantum physics-informed neural networks |
| title_fullStr | General implementation of quantum physics-informed neural networks |
| title_full_unstemmed | General implementation of quantum physics-informed neural networks |
| title_short | General implementation of quantum physics-informed neural networks |
| title_sort | general implementation of quantum physics informed neural networks |
| topic | Physics Machine learning Quantum computing Physics informed neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2590005623000127 |
| work_keys_str_mv | AT shashankreddyvadyala generalimplementationofquantumphysicsinformedneuralnetworks AT sainethrabetgeri generalimplementationofquantumphysicsinformedneuralnetworks |