Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids
Abstract
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.
Authors 6
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Priyanka Arkalgud Ganeshamurthy Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems
Affiliation as printed
E.ON Energy Research Center , Institute for Automation of Complex Power Systems , RWTH Aachen University , Aachen , Germany
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Affiliation as printed
E.ON Digital Technology GmbH , Hannover , Germany
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Affiliation as printed
E.ON Digital Technology GmbH , Hannover , Germany
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Affiliation as printed
E.ON Group Innovation GmbH , Essen , Germany
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Antonello Monti Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems
Affiliation as printed
E.ON Energy Research Center , Institute for Automation of Complex Power Systems , RWTH Aachen University , Aachen , Germany
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