4-8 July 2016
Kramer Law building
Africa/Johannesburg timezone
The Proceedings of SAIP2016 published on 24 December 2017
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Solar power prediction model using quantum machine learning algorithm

Presented by Mr. Makhamisa SENEKANE on 6 Jul 2016 from 14:20 to 14:40
Type: Oral Presentation
Track: Track F - Applied Physics

Abstract

Classical machine learning is the intersection of artificial intelligence and statistics. It studies the algorithms that can be used to analyze data and also make predictions about the data. The quantum version of classical machine learning is Quantum Machine Learning (QML). As a sub-field of quantum computing, it uses quantum mechanical concepts such as superposition, entanglement and quantum adiabatic theorem to analyze data and make predictions about data. Currently, QML research has taken two directions. The first approach involves implementing the computationally expensive subroutines of classical machine learning algorithms on a quantum computer. The second approach concerns using classical machine learning algorithms on quantum information. In this paper, we propose a solar power prediction algorithm which implements quantum support vector algorithm. Simulation results underline the utility of this prediction model.

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Paper

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Place

Location: Kramer Law building
Address: UCT Middle Campus Cape Town
Room: 4B


Primary authors

  • Mr. Makhamisa SENEKANE Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa
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