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Optimization problems in disciplines such as machine learning are commonly solved with iterative methods.
Convex programming in hilbert space
Alan A Goldstein · 1964
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Constrained minimization methods
Evgeny S Levitin and Boris T Polyak · 1966
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Projected newton methods for optimization problems with simple constraints
Dimitri P. Bertsekas · 1982
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The central limit theorem for dependent random variables
W. Hoeffding and H. Robbins · 1994
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A quantum algorithm for finding the minimum
C. Dürr and P. Hoyer · 1996
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Quantum computation by adiabatic evolution
Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser · 2000
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Creating superpositions that correspond to efficiently integrable probability distributions
L. Grover and T. Rudolph · 2002
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Quantum amplitude amplification and estimation
Gilles Brassard, Peter Hoyer, Michele Mosca, and Alain Tapp · 2002
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Fast quantum algorithm for numerical gradient estimation
Stephen P Jordan · 2005
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Numerical Optimization
J. Nocedal and S. Wright · 2006
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Efficient state preparation for a register of quantum bits
Andrei N Soklakov and Rüdiger Schack · 2006
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A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Quantum random access memory
V. Giovannetti, S. Lloyd, and L. Maccone · 2008
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Architectures for a quantum random access memory
V. Giovannetti, S. Lloyd, and L. Maccone · 2008
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Training a large scale classifier with the quantum adiabatic algorithm
Hartmut Neven, Vasil S Denchev, Geordie Rose, and William G Macready · 2009
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Quantum algorithm for linear systems of equations
Aram W Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
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Experimental quantum private queries with linear optics
F. De Martini, V. Giovannetti, S. Lloyd, L. Maccone, E. Nagali, L. Sansoni, and F. Sciarrino · 2009
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Learning deep architectures for ai
Yoshua Bengio · 2009
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Deep learning via hessian-free optimization
James Martens · 2010
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Approximation algorithms for homogeneous polynomial optimization with quadratic constraints
Simai He, Zhening Li, and Shuzhong Zhang · 2010
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A. Ambainis · 2010
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, and Yoshua Bengio · 2014
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Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, and Alejandro Perdomo-Ortiz · 2015
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Quantum linear systems algorithm with exponentially improved dependence on precision
A M. Childs, R. Kothari, and R. D. Somma · 2015
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Quantum supremacy through the quantum approximate optimization algorithm
Edward Farhi and Aram W Harrow · 2016
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Prediction by linear regression on a quantum computer
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Black-box hamiltonian simulation and unitary implementation
Dominic W. Berry and Andrew M. Childs · 2012
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Proximal newton-type methods for convex optimization
Jason D. Lee, Yuekai Sun, and Michael A. Saunders · 2012
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Adam D Bookatz · 2012
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Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione · 2016
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Hamiltonian simulation by qubitization
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