Transcription of Quantum Machine Learning
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Quantum Machine Learning
Quantum Machine LearningJacob Biamonte1,2,*, Peter Wittek3, Nicola Pancotti4, PatrickRebentrost5, Nathan Wiebe6, and Seth Software Initiative, Skolkovo Institute of Science andTechnology, Skoltech Building 3, Moscow 143026, Russia2 Institute for Quantum Computing, University of Waterloo,Waterloo, N2L 3G1 Ontario, Canada3 ICFO-The Institute of Photonic Sciences, Castelldefels (Barcelona),08860 Spain4 Max Planck Institute of Quantum Optics , Hans-Kopfermannstr. 1,D-85748 Garching, Germany5 Massachusetts Institute of Technology, Research Laboratory ofElectronics, Cambridge, MA 021396 Station Q Quantum Architectures and Computation Group,Microsoft Research, Redmond WA 980527 Massachusetts Institute of Technology, Department of MechanicalEngineering, Cambridge MA 02139 USAMay 14, 2018 AbstractFuelled by increasing computer power and algorithmic advances, ma-chine Learning techniques have become powerful tools for finding patternsin data.
machine learning would rely on the existence of a quantum computer and is a so called, benchmarking problem. Such advantages could include improved classi - cation accuracy and sampling of classically inaccessible systems. Accordingly, quantum speedups in machine learning are currently characterized using idealized
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