The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Francesco Petruccione - One of the best experts on this subject based on the ideXlab platform.
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Quantum computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Akihiro Minagawa, Francesco Petruccione, Jun Sun, Wei Fan, Song Wang, Liang XuAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.
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PRICAI - Quantum Computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Francesco PetruccioneAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger’s proposal for measuring the Hamming distance on a quantum computer [CA Trugenberger, Phys Rev Let 87, 2001] and discuss its advantages using handwritten digit recognition as from the MNIST database.
Maria Schuld - One of the best experts on this subject based on the ideXlab platform.
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Quantum computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Akihiro Minagawa, Francesco Petruccione, Jun Sun, Wei Fan, Song Wang, Liang XuAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.
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PRICAI - Quantum Computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Francesco PetruccioneAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger’s proposal for measuring the Hamming distance on a quantum computer [CA Trugenberger, Phys Rev Let 87, 2001] and discuss its advantages using handwritten digit recognition as from the MNIST database.
Liang Xu - One of the best experts on this subject based on the ideXlab platform.
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Quantum computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Akihiro Minagawa, Francesco Petruccione, Jun Sun, Wei Fan, Song Wang, Liang XuAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.
Ilya Sinayskiy - One of the best experts on this subject based on the ideXlab platform.
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Quantum computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Akihiro Minagawa, Francesco Petruccione, Jun Sun, Wei Fan, Song Wang, Liang XuAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.
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PRICAI - Quantum Computing for Pattern Classification
Lecture Notes in Computer Science, 2014Co-Authors: Maria Schuld, Ilya Sinayskiy, Francesco PetruccioneAbstract:It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of Pattern Classification. We introduce a quantum Pattern Classification algorithm that draws on Trugenberger’s proposal for measuring the Hamming distance on a quantum computer [CA Trugenberger, Phys Rev Let 87, 2001] and discuss its advantages using handwritten digit recognition as from the MNIST database.
C. Jansuwan - One of the best experts on this subject based on the ideXlab platform.
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Dynamic projection network for supervised Pattern Classification
International Journal of Approximate Reasoning, 2005Co-Authors: C. JansuwanAbstract:This paper describes the development of the utility of a dynamic neural network known as projection network for Pattern Classification. It first gives the derivation of the projection network, and then describes the network architecture and analyzes properties such as equilibrium points and their stability condition. The procedures for utilizing the projection network for Pattern Classification are established and the benefits are discussed. The proposed Classification system is then tested with well-known benchmark data sets, namely the Fisher's iris data, the heart disease data and the credit screening data and the results are compared to other classifiers including Neural Network Rule Base (NNRB), Genetic Algorithm Rule Base (GARB), Rough Set, and C4.5 decision tree. The projection network was proven to be a viable alternative to existing methods.
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Dynamic Projection Network for Supervised Pattern Classification
Dynamic Systems and Control Parts A and B, 2004Co-Authors: C. JansuwanAbstract:This paper describes the development of the utility of a dynamic neural network known as projection network for Pattern Classification. It first gives the derivation of the projection network, and then describes the network architecture and analyzes properties such as equilibrium points and their stability condition. The procedures for utilizing the projection network for Pattern Classification problem are established and the benefits are discussed. The proposed Classification system is then tested with well-known benchmark data sets, namely the Fisher’s iris data, the heart disease data and the credit screening data and the results are compared to other classifiers including Neural Network Rule Base (NNRB), Genetic Algorithm Rule Base (GARB), Rough Set, and C4.5 decision tree.Copyright © 2004 by ASME
