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Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques.
Dynamic programming
Richard Bellman · 1966
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Refinements of the multidimensional central limit theorem and applications
R. N. Bhattacharya · 1977
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A berry-esseen bound for symmetric statistics
Willem R van Zwet · 1984
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A berry-esseen bound for functions of independent random variables
Karl O Friedrich et al · 1989
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Neural network ensembles
Lars Kai Hansen and Peter Salamon · 1990
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Rapid Solution of Problems by Quantum Computation
D. Deutsch and R. Jozsa · 1992
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Model selection and accounting for model uncertainty in graphical models using occam’s window
David Madigan and Adrian E Raftery · 1994
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On the power of quantum computation
Daniel R Simon · 1997
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Bayesian model averaging: a tutorial
Jennifer A Hoeting, David Madigan, Adrian E Raftery, and Chris T Volinsky · 1999
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When is “nearest neighbor” meaningful?
Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, and Uri Shaft · 1999
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Quantum computation and quantum information, 2002
Michael A Nielsen and Isaac Chuang · 2002
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The boosting approach to machine learning: An overview
Robert E Schapire · 2003
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Implementation of the deutsch–jozsa algorithm on an ion-trap quantum computer
Stephan Gulde, Mark Riebe, Gavin PT Lancaster, Christoph Becher, Jürgen Eschner, Hartmut Häffner, Ferdinand Schmidt-Kaler, Isaac L Chuang, and Rainer Blatt · 2003
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Generalized accept-reject sampling schemes
George Casella, Christian P Robert, Martin T Wells, et al · 2004
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Evolving hybrid ensembles of learning machines for better generalisation
Arjun Chandra and Xin Yao · 2006
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Theory of nearest neighbors indexability
Uri Shaft and Raghu Ramakrishnan · 2006
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High-dimensional distributions with convexity properties
Bo’az Klartag · 2010
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Quantum autoencoders for efficient compression of quantum data
Jonathan Romero, Jonathan P Olson, and Alan Aspuru-Guzik · 2017
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A quantum algorithm to train neural networks using low-depth circuits
Guillaume Verdon, Michael Broughton, and Jacob Biamonte · 2017
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Quantum ensembles of quantum classifiers
Maria Schuld and Francesco Petruccione · 2018
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Quantum-assisted helmholtz machines: a quantum–classical deep learning framework for industrial datasets in near-term devices
Marcello Benedetti, John Realpe-Gómez, and Alejandro Perdomo-Ortiz · 2018
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Ensemble machine learning: methods and applications
Cha Zhang and Yunqian Ma · 2012
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A survey on unsupervised outlier detection in high-dimensional numerical data
Arthur Zimek, Erich Schubert, and Hans-Peter Kriegel · 2012
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Ensemble machine learning: methods and applications
Cha Zhang and Yunqian Ma · 2012
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien · 2014
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Supervised Learning with Quantum Computers
Maria Schuld and Francesco Petruccione · 2018
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Supervised learning with quantum computers, 2018
Maria Schuld and Francesco Petruccione · 2018
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Quantum machine learning in feature hilbert spaces
Maria Schuld and Nathan Killoran · 2019
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Quantum mean embedding of probability distributions
Jonas M Kübler, Krikamol Muandet, and Bernhard Schölkopf · 2019
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Supervised learning with quantum-enhanced feature spaces
Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta · 2019
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