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Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term ``killer application''.
“Parameterized quantum circuits as machine learning models”
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 1906
Earlier work this paper cites.
“Quantum supremacy using a programmable superconducting processor”
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al · 1910
Earlier work this paper cites.
“Efficient distribution-free learning of probabilistic concepts”
Michael J Kearns and Robert E Schapire · 1994
Earlier work this paper cites.
“The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network”
Peter L Bartlett · 1998
Earlier work this paper cites.
“Structural risk minimization over data-dependent hierarchies”
John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, and Martin Anthony · 1998
Earlier work this paper cites.
“Prediction, learning, uniform convergence, and scale-sensitive dimensions”
Peter L Bartlett and Philip M Long · 1998
Earlier work this paper cites.
“Function learning from interpolation”
Martin Anthony and Peter L Bartlett · 2000
Earlier work this paper cites.
“Pseudo-dimension of quantum circuits”
Matthias C Caro and Ishaun Datta · 2002
Earlier work this paper cites.
“Learning with kernels: support vector machines, regularization, optimization, and beyond”
Bernhard Schölkopf, Alexander J Smola, Francis Bach, et al · 2002
Earlier work this paper cites.
“Adaptive quantum computation, constant depth quantum circuits and arthur-merlin games”
Barbara M Terhal and David P DiVincenzo · 2004
Earlier work this paper cites.
“The learnability of quantum states”
Scott Aaronson · 2007
Earlier work this paper cites.
“A rigorous and robust quantum speed-up in supervised machine learning”
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2010
Earlier work this paper cites.
Michael J Bremner, Richard Jozsa, and Dan J Shepherd · 2011
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“The power of quantum neural networks”
Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, and Stefan Woerner · 2011
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“Power of data in quantum machine learning”
Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R McClean · 2011
Cited alongside, same era.
“Variational quantum algorithms”
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al · 2012
Cited alongside, same era.
“Hierarchical quantum classifiers”
Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G Green, and Simone Severini · 2018
Later among the works it cites.
“Foundations of machine learning”
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Later among the works it cites.
“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
Later among the works it cites.
“Quantum machine learning in feature Hilbert spaces”
Maria Schuld and Nathan Killoran · 2019
Later among the works it cites.
“Mathematical foundations of supervised learning”
Michael M Wolf · 2020
Later among the works it cites.
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Jae-Eun Park, Brian Quanz, Steve Wood, Heather Higgins, and Ray Harishankar · 2012
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“A quantum approximate optimization algorithm” (2014)
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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“On the uniform convergence of relative frequencies of events to their probabilities”
Vladimir N Vapnik and A Ya Chervonenkis · 2015
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“The theory of variational hybrid quantum-classical algorithms”
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik · 2016
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“Scalable quantum simulation of molecular energies”
Peter JJ O’Malley, Ryan Babbush, Ian D Kivlichan, Jonathan Romero, Jarrod R McClean, Rami Barends, Julian Kelly, Pedram Roushan, Andrew Tranter, Nan Ding, et al · 2016
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“Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets”
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta · 2017
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“Demonstration of quantum advantage in machine learning”
Diego Ristè, Marcus P Da Silva, Colm A Ryan, Andrew W Cross, Antonio D Córcoles, John A Smolin, Jay M Gambetta, Jerry M Chow, and Blake R Johnson · 2017
Cited alongside, same era.
“Quantum computing in the NISQ era and beyond”
John Preskill · 2018
Cited alongside, same era.
“Quantum feature space learning: characterisation and possible advantages”
Dyon van Vreumingen · 2020
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“Supervised quantum machine learning models are kernel methods” (2021)
Maria Schuld · 2021
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“On the statistical complexity of quantum circuits” (2021)
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“Effects of quantum resources on the statistical complexity of quantum circuits” (2021)
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“Rademacher complexity of noisy quantum circuits” (2021)
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“Generalization in quantum machine learning: A quantum information standpoint”
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“Efficient measure for the expressivity of variational quantum algorithms”
Yuxuan Du, Zhuozhuo Tu, Xiao Yuan, and Dacheng Tao · 2022
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