Journal ArticleDOI
Quantum optimization for training support vector machines
Davide Anguita,Sandro Ridella,Fabio Rivieccio,Rodolfo Zunino +3 more
- Vol. 16, Iss: 5, pp 763-770
TLDR
The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations, and compares the behavioral aspects of conventional and enhanced SVMs.Abstract:
Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered.read more
Citations
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Journal ArticleDOI
Quantum machine learning
Jacob Biamonte,Jacob Biamonte,Peter Wittek,Nicola Pancotti,Patrick Rebentrost,Nathan Wiebe,Seth Lloyd +6 more
TL;DR: The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers.
Journal ArticleDOI
Machine learning & artificial intelligence in the quantum domain: a review of recent progress.
TL;DR: In this article, the authors describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain, and discuss the fundamental issue of quantum generalizations of learning and AI concepts.
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Quantum Machine Learning: What Quantum Computing Means to Data Mining
TL;DR: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning by paring down the complexity of the disciplines involved.
Posted Content
Machine learning \& artificial intelligence in the quantum domain
Vedran Dunjko,Hans J. Briegel +1 more
TL;DR: The main ideas, recent developments and progress are described in a broad spectrum of research investigating ML and AI in the quantum domain, investigating how results and techniques from one field can be used to solve the problems of the other.
Journal ArticleDOI
Quantum speed-up for unsupervised learning
TL;DR: It is explained how it is possible to accelerate learning algorithms by quantizing some of their subroutines by giving quantized versions of clustering via minimum spanning tree, divisive clustering and k-medians that are faster than their classical analogues.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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Quantum Computation and Quantum Information
TL;DR: In this article, the quantum Fourier transform and its application in quantum information theory is discussed, and distance measures for quantum information are defined. And quantum error-correction and entropy and information are discussed.