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Quantum machine learning carries the promise to revolutionize information and communication technologies.
A trace minimization algorithm for the generalized eigenvalue problem
Ahmed H Sameh and John A Wisniewski · 1982
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Methods of combining multiple classifiers and their applications to handwritten digit recognition
C Kaynak · 1995
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning invariance manifolds
Wiskott Laurenz and Laurenz Wiskott · 1999
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Information theory, inference and learning algorithms
David JC MacKay · 2002
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Independent slow feature analysis and nonlinear blind source separation
Tobias Blaschke and Laurenz Wiskott · 2004
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Fast monte-carlo algorithms for finding low-rank approximations
Alan Frieze, Ravi Kannan, and Santosh Vempala · 2004
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Pattern Recognition with Slow Feature Analysis
Pietro Berkes · 2005
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Slow feature analysis yields a rich repertoire of complex cell properties
P. Berkes and L. Wiskott · 2005
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Incremental learning for robust visual tracking
David A Ross, Jongwoo Lim, Ruei-Sung Lin, and Ming-Hsuan Yang · 2008
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Understanding Slow Feature Analysis: A Mathematical Framework
Henning Sprekeler and Laurenz Wiskott · 2008
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Quantum Algorithm for Linear Systems of Equations
Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd · 2009
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
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Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
Stefan Klampfl and Wolfgang Maass · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Scikit-learn: Machine Learning in {P}ython
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot, and E Duchesnay · 2011
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On the Relation of Slow Feature Analysis and Laplacian Eigenmaps
Henning Sprekeler · 2011
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Slow feature analysis
L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler, and N. Wilbert · 2011
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Hamiltonian simulation using linear combinations of unitary operations
Andrew M Childs and Nathan Wiebe · 2012
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Slow feature analysis: Perspectives for technical applications of a versatile learning algorithm
Alberto N Escalante-B and Laurenz Wiskott · 2012
Cited alongside, same era.
Quantum Algorithm for Data Fitting
Nathan Wiebe, Daniel Braun, and Seth Lloyd · 2012
Cited alongside, same era.
Slow Feature Analysis for Human Action Recognition
Zhang Zhang and Dacheng Tao · 2012
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Supervised slow feature analysis for face recognition
Xingjian Gu, Chuancai Liu, and Sheng Wang · 2013
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Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2013
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Quantum Algorithms for Linear Algebra and Machine Learning
Anupam Prakash · 2014
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Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
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SciPy: Open source scientific tools for Python, 2001-2018
Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2018
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A quantum interior point method for lps and sdps
Iordanis Kerenidis and Anupam Prakash · 2018
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Quantum machine learning for quantum anomaly detection
Nana Liu and Patrick Rebentrost · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Quantum support vector machine for big data classification
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd · 2014
Cited alongside, same era.
Dl-sfa: deeply-learned slow feature analysis for action recognition
Lin Sun, Kui Jia, Tsung-Han Chan, Yuqiang Fang, Gang Wang, and Shuicheng Yan · 2014
Cited alongside, same era.
An extension of slow feature analysis for nonlinear blind source separation
Henning Sprekeler, Tiziano Zito, and Laurenz Wiskott · 2014
Cited alongside, same era.
Quantum discriminant analysis for dimensionality reduction and classification
Iris Cong and Luming Duan · 2016
Cited alongside, same era.
Quantum algorithms for topological and geometric analysis of data
Seth Lloyd, Silvano Garnerone, and Paolo Zanardi · 2016
Cited alongside, same era.
Quantum-assisted learning of hardware-embedded probabilistic graphical models
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, and Alejandro Perdomo-Ortiz · 2017
Cited alongside, same era.
Patrick Rebentrost and Seth Lloyd · 2018
Closest in time.
Circuit-centric quantum classifiers
Maria Schuld, Alex Bocharov, Krysta Svore, and Nathan Wiebe · 2018
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Quantum-inspired classical algorithms for principal component analysis and supervised clustering
Ewin Tang · 2018
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Quantum-inspired algorithms in practice
Juan Miguel Arrazola, Alain Delgado, Bhaskar Roy Bardhan, and Seth Lloyd · 2019
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Optimal usage of quantum random access memory in quantum machine learning
Jeongho Bang, Arijit Dutta, Seung-Woo Lee, and Jaewan Kim · 2019
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, and Stefan Sack · 2019
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The power of block-encoded matrix powers: Improved regression techniques via faster hamiltonian simulation
Shantanav Chakraborty, András Gilyén, and Stacey Jeffery · 2019
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Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics
András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe · 2019
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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
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Hardware-efficient quantum random access memory with hybrid quantum acoustic systems
Connor T Hann, Chang-Ling Zou, Yaxing Zhang, Yiwen Chu, Robert J Schoelkopf, Steven M Girvin, and Liang Jiang · 2019
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Experimental realization of 105-qubit random access quantum memory
N Jiang, Y-F Pu, W Chang, C Li, S Zhang, and L-M Duan · 2019
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Circuit-based quantum random access memory for classical data
Daniel K Park, Francesco Petruccione, and June-Koo Kevin Rhee · 2019
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A quantum-inspired classical algorithm for recommendation systems
Ewin Tang · 2019
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