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 fo...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.展开更多
We propose and demonstrate an optical implementation of a quantum key distribution protocol, which uses three-non-orthogonal states and six states in total. The proposed scheme improves the protocol that is proposed b...We propose and demonstrate an optical implementation of a quantum key distribution protocol, which uses three-non-orthogonal states and six states in total. The proposed scheme improves the protocol that is proposed by Phoenix, Barnett and Chefles [J. Mod. Opt. 47, 507 (2000)]. An additional feature, which we introduce in our scheme, is that we add another detection set;where each detection set has three non-orthogonal states. The inclusion of an additional detection set leads to improved symmetry, increased eavesdropper detection and higher security margin for our protocol.展开更多
文摘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.
文摘We propose and demonstrate an optical implementation of a quantum key distribution protocol, which uses three-non-orthogonal states and six states in total. The proposed scheme improves the protocol that is proposed by Phoenix, Barnett and Chefles [J. Mod. Opt. 47, 507 (2000)]. An additional feature, which we introduce in our scheme, is that we add another detection set;where each detection set has three non-orthogonal states. The inclusion of an additional detection set leads to improved symmetry, increased eavesdropper detection and higher security margin for our protocol.
