Abstract
Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and also make some forecasts based on the given data. Classical machine learning has its quantum part, which is known as quantum machine learning (QML). QML, which is a field of quantum computing, uses some of the quantum mechanical principles and concepts which include superposition, entanglement and quantum adiabatic theorem to assess the data and make some forecasts based on the data. At the present moment, research in QML has taken two main approaches. 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 a quantum information, to speed up performance of the algorithms. The work presented in this manuscript proposes a quantum support vector algorithm that can be used to forecast solar irradiation. The novelty of this work is in using quantum mechanical principles for application in machine learning. Python programming language was used to simulate the performance of the proposed algorithm on a classical computer. Simulation results that were obtained show the usefulness of this algorithm for predicting solar irradiation.
Highlights
Machine learning is a subfield of artificial intelligence
Classical machine learning, which is at the intersection of artificial intelligence and statistics, investigates and formulates algorithms which can be used to discover patterns in the given data and make some forecasts based on the given data
In the work reported in this paper, we used the first approach to model solar power using quantum Support Vector Machine (SVM)
Summary
There has been a concerted effort to explore the benefits of using QIP for machine learning applications This results in the field of Quantum Machine Learning (QML). It has been demonstrated that QML techniques provide a performance speedup compared to their classical counterparts [11] [15] This speedup is the major motivation for exploring QML algorithms. The second approach involves making use of quantum mechanical principles in order to design machine learning algorithms for classical computers. We provide background information on machine learning, QIP and QML.
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