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Quantum machine learning is arguably one of the most explored applications of near-term quantum devices.
“Expressive power of tensor-network factorizations for probabilistic modeling”
Ivan Glasser et al · 1907
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“Random Features for Large-Scale Kernel Machines”
Ali Rahimi and Benjamin Recht · 2007
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“Support vector machines”
Ingo Steinwart and Andreas Christmann · 2008
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“On Learning with Integral Operators”
Lorenzo Rosasco, Mikhail Belkin and Ernesto Vito · 2010
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“The density-matrix renormalization group in the age of matrix product states”
Ulrich Schollwöck · 2010
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“Minimally entangled typical thermal state algorithms”
E Stoudenmire and Steven White · 2010
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“Perfect sampling with unitary tensor networks”
Andrew. Ferris and Guifre Vidal · 2012
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“Optimal Rates for Random Fourier Features”
Bharath Sriperumbudur and Zoltan Szabo · 2015
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“On the Error of Random Fourier Features”, 2015
Danica. Sutherland and Jeff Schneider · 2015
Earlier work this paper cites.
“Random Features for Sparse Signal Classification”
Jen-Hao Chang, Aswin. Sankaranarayanan and B… Kumar · 2016
Earlier work this paper cites.
“Introduction to property testing”
Oded Goldreich · 2017
Earlier work this paper cites.
“Generalization Properties of Learning with Random Features”
Alessandro Rudi and Lorenzo Rosasco · 2017
Cited alongside, same era.
“Foundations of machine learning”
Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar · 2018
Cited alongside, same era.
“Parameterized quantum circuits as machine learning models”
Marcello Benedetti, Erika Lloyd, Stefan Sack and Mattia Fiorentini · 2019
Cited alongside, same era.
“Input redundancy for parameterized quantum circuits”
Francisco Gil and Dirk Theis · 2020
Cited alongside, same era.
“Variational quantum algorithms”
Marco Cerezo et al · 2021
Cited alongside, same era.
“Encoding-dependent generalization bounds for parametrized quantum circuits”
Matthias. Caro et al · 2021
Cited alongside, same era.
“Group-Invariant Quantum Machine Learning”
Martín Larocca et al · 2022
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“Classically approximating variational quantum machine learning with random Fourier features”
Jonas Landman et al · 2022
Later among the works it cites.
“Efficient classical algorithms for simulating symmetric quantum systems”
Eric. Anschuetz, Andreas Bauer, Bobak. Kiani and Seth Lloyd · 2023
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“Classical simulations of noisy variational quantum circuits”, 2023
Enrico Fontana et al · 2023
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“Quantum machine learning beyond kernel methods”
Sofiene Jerbi et al · 2023
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“Exploiting Symmetry in Variational Quantum Machine Learning”
Johannes Meyer et al · 2023
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“Lower and Upper Bounds on the VC-Dimension of Tensor Network Models”, 2021
Behnoush Khavari and Guillaume Rabusseau · 2021
Cited alongside, same era.
“Supervised quantum machine learning models are kernel methods”
Maria Schuld · 2021
Cited alongside, same era.
“Effect of data encoding on the expressive power of variational quantum-machine-learning models”
Maria Schuld, Ryan Sweke and Johannes Meyer · 2021
Cited alongside, same era.
“Spectral analysis for noise diagnostics and filter-based digital error mitigation”, 2022
Enrico Fontana, Ivan Rungger, Ross Duncan and Cristina Cîrstoiu · 2022
Cited alongside, same era.
“Classical surrogates for quantum learning models”
Franz. Schreiber, Jens Eisert and Johannes Meyer · 2023
Closest in time.
“Simulating quantum mean values in noisy variational quantum algorithms: A polynomial-scale approach”, 2023
Yuguo Shao, Fuchuan Wei, Song Cheng and Zhengwei Liu · 2023
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“Shadows of quantum machine learning”
Sofiene Jerbi et al · 2024
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“Dequantizing quantum machine learning models using tensor networks”
Seongwook Shin, Yong Teo and Hyunseok Jeong · 2024
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