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Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings.
On the multiple descent of minimum-norm interpolants and restricted lower isometry of kernels
Tengyuan Liang, Alexander Rakhlin, and Xiyu Zhai · 1908
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Statistical mechanics of learning from examples
H. S. Seung, H. Sompolinsky, and N. Tishby · 1992
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Statistical mechanics of support vector networks
Rainer Dietrich, Manfred Opper, and Haim Sompolinsky · 1999
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf, Alexander J Smola, Francis Bach, et al · 2002
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The curse of dimensionality for local kernel machines
Yoshua Bengio, Olivier Delalleau, and Nicolas Le Roux · 2005
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On the eigenspectrum of the Gram matrix and the generalization error of kernel-pca
J. Shawe-Taylor, C.K.I. Williams, N. Cristianini, and J. Kandola · 2005
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Temporally unstructured quantum computation
Dan J. Shepherd and Michael J. Bremner · 2008
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Information, Physics, and Computation
M. Mezard and A. Montanari · 2009
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On learning with integral operators
Lorenzo Rosasco, Mikhail Belkin, and Ernesto De Vito · 2010
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Quantum Computation and Quantum Information
Michael A Nielsen and Isaac L Chuang · 2011
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Statistical mechanics of complex neural systems and high dimensional data
Madhu Advani, Subhaneil Lahiri, and Surya Ganguli · 2013
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Random matrix techniques in quantum information theory
Benoît Collins and Ion Nechita · 2016
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Symbolic integration with respect to the Haar measure on the unitary group
Zbigniew Puchała and Jarosław Adam Miszczak · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Deep learning for classical Japanese literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha · 2018
Cited alongside, same era.
Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
Cited alongside, same era.
Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
Cited alongside, same era.
Quantum circuit learning
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii · 2018
Cited alongside, same era.
Density estimation for statistics and data analysis
Bernard W Silverman · 2018
Cited alongside, same era.
Covariant quantum kernels for data with group structure
Jennifer R. Glick, Tanvi P. Gujarati, Antonio D. Corcoles, Youngseok Kim, Abhinav Kandala, Jay M. Gambetta, and Kristan Temme · 2021
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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 · 2021
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Training quantum embedding kernels on near-term quantum computers
Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster, Peter-Jan H. S. Derks, Paul K. Faehrmann, and Johannes Jakob Meyer · 2021
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The inductive bias of quantum kernels
Jonas Kübler, Simon Buchholz, and Bernhard Schölkopf · 2021
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A rigorous and robust quantum speed-up in supervised machine learning
Yunchao Liu, Srinivasan Arunachalam, and Kristan Temme · 2021
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The PLAsTiCC team, Tarek Allam, Anita Bahmanyar, Rahul Biswas, Mi Dai, Lluís Galbany, Renée Hlo v · 2018
Cited alongside, same era.
The Theory of Quantum Information
John Watrous · 2018
Cited alongside, same era.
Qiskit: An open-source framework for quantum computing
Héctor Abraham, Ismail Yunus Akhalwaya, Gadi Aleksandrowicz, Thomas Alexander, G Alexandrowics, E Arbel, A Asfaw, C Azaustre, P Barkoutsos, G Barron, et al · 2019
Cited alongside, same era.
Supervised learning with quantum-enhanced feature spaces
Vojt v · 2019
Cited alongside, same era.
Sublinear quantum algorithms for training linear and kernel-based classifiers
Tongyang Li, Shouvanik Chakrabarti, and Xiaodi Wu · 2019
Cited alongside, same era.
Quantum machine learning in feature Hilbert spaces
Maria Schuld and Nathan Killoran · 2019
Cited alongside, same era.
Spectrum dependent learning curves in kernel regression and wide neural networks
Blake Bordelon, Abdulkadir Canatar, and Cengiz Pehlevan · 2020
Cited alongside, same era.
Machine learning of high dimensional data on a noisy quantum processor
Evan Peters, João Caldeira, Alan Ho, Stefan Leichenauer, Masoud Mohseni, Hartmut Neven, Panagiotis Spentzouris, Doug Strain, and Gabriel N. Perdue · 2021
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Supervised quantum machine learning models are kernel methods
Maria Schuld · 2021
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Importance of kernel bandwidth in quantum machine learning
Ruslan Shaydulin and Stefan M. Wild · 2021
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Towards understanding the power of quantum kernels in the NISQ era
Xinbiao Wang, Yuxuan Du, Yong Luo, and Dacheng Tao · 2021
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Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan, Chen Zhou, Jay Chan, Chi Lung Cheng, Tuan Pham, Yan Qian, Alex Zeng Wang, Rui Zhang, Miron Livny, Jennifer Glick, Panagiotis Kl. Barkoutsos, Stefan Woerner, Ivano Tavernelli, Federico Carminati, Alberto Di Meglio, Andy C. Y. Li, Joseph Lykken, Panagiotis Spentzouris, Samuel Yen-Chi Chen, Shinjae Yoo, and Tzu-Chieh Wei · 2021
Later among the works it cites.
Valentin Heyraud, Zejian Li, Zakari Denis, Alexandre Le Boité, and Cristiano Ciuti · 2022
Closest in time.
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, M. Cerezo, and Patrick J. Coles · 2022
Closest in time.
Exponential concentration and untrainability in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo, and Zoë Holmes · 2022
Closest in time.
Gaussian initializations help deep variational quantum circuits escape from the barren plateau
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, and Dacheng Tao · 2022
Closest in time.