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, Russia2Institute for Quantum Computing, University of Waterloo,Waterloo, N2L 3G1 Ontario, Canada3ICFO-The Institute of Photonic Sciences, Castelldefels (Barcelona),08860 Spain4Max Planck Institute of Quantum Optics , Hans-Kopfermannstr. 1,D-85748 Garching, Germany5Massachusetts Institute of Technology, Research Laboratory ofElectronics, Cambridge, MA 021396Station Q Quantum Architectures and Computation Group,Microsoft Research, Redmond WA 980527Massachusetts Institute of Technology, Department of MechanicalEngineering, Cambridge MA 02139 USAMay 14, 2018AbstractFuelled by increasing computer power and algorithmic advances, ma-chine Learning techniques have become powerful tools for finding patternsin data.
performing matrix operations on vectors in a high dimensional vector space. But quantum mechanics is all about matrix operations on vectors in high dimensional vector spaces. The key ingredient behind these methods is that the quantum state of n quantum bits or qubits is a vector in a 2n-dimensional complex vector space;
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