Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909389043007488 |
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| author | Ganeshamurthy, Priyanka Arkalgud Ghosh, Kumar O'Meara, Corey Cortiana, Giorgio Schiefelbein-Lach, Jan Monti, Antonello |
| author_facet | Ganeshamurthy, Priyanka Arkalgud Ghosh, Kumar O'Meara, Corey Cortiana, Giorgio Schiefelbein-Lach, Jan Monti, Antonello |
| contents | Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids Ganeshamurthy, Priyanka Arkalgud Ghosh, Kumar O'Meara, Corey Cortiana, Giorgio Schiefelbein-Lach, Jan Monti, Antonello Quantum Physics Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling using GPs, the computational complexity associated with GPs demanding immense resources make their practical usage highly challenging. In this article, we develop a quantum version of multi-output Gaussian Process (QGP) by implementing a well-known quantum algorithm called HHL, to perform the Kernel matrix inversion within the Gaussian Process. To reduce the large circuit depth of HHL a circuit optimization technique called Approximate Quantum Compiling (AQC) has been implemented. We further showcase the application of QGP for a real-world problem to estimate line parameters of an electrical grid. Using AQC, up to 13-qubit HHL circuit has been implemented for a 32x32 kernel matrix inversion on IBM Quantum hardware for demonstrating QGP based line parameter estimation experimentally. Finally, we compare its performance against noise-less quantum simulators and classical computation results. |
| title | Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2411.09123 |