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Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis.
Y. LeCun, The mnist database of handwritten digits, http://yann. lecun. com/exdb/mnist/ (1998)
1998
Earlier work this paper cites.
M. Kline, Calculus: An Intuitive and Physical Approach (Dover publications, INC., 1998)
1998
Earlier work this paper cites.
N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. Kandola, On kernel-target alignment, Advances in Neural Information Processing Systems 14
2001
Earlier work this paper cites.
T. Hofmann, B. Schölkopf, and A. J. Smola, Kernel methods in machine learning, The annals of statistics 36
2008
Earlier work this paper cites.
R. A. Low, Large deviation bounds for k-designs, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465
2009
Earlier work this paper cites.
A. B. Tsybakov, Introduction to Nonparametric Estimation (Springer, 2009)
2009
Earlier work this paper cites.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature 549
2017
Earlier work this paper cites.
Y. Li and S. C. Benjamin, Efficient variational quantum simulator incorporating active error minimization, Phys. Rev. X 7
2017
Earlier work this paper cites.
K. Temme, S. Bravyi, and J. M. Gambetta, Error mitigation for short-depth quantum circuits, Physical review letters 119
2017
Earlier work this paper cites.
D. A. Roberts and B. Yoshida, Chaos and complexity by design, Journal of High Energy Physics 2017
2017
Earlier work this paper cites.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nature Communications 9
2018
Earlier work this paper cites.
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning (MIT Press, 2018)
2018
Earlier work this paper cites.
S. Endo, S. C. Benjamin, and Y. Li, Practical quantum error mitigation for near-future applications, Physical Review X 8
2018
Earlier work this paper cites.
V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, Supervised learning with quantum-enhanced feature spaces, Nature 567
2019
Earlier work this paper cites.
S. Sim, P. D. Johnson, and A. Aspuru-Guzik, Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, Advanced Quantum Technologies 2
2019
Earlier work this paper cites.
A. Kandala, K. Temme, A. D. Córcoles, A. Mezzacapo, J. M. Chow, and J. M. Gambetta, Error mitigation extends the computational reach of a noisy quantum processor, Nature 567
2019
Earlier work this paper cites.
J. Tangpanitanon, S. Thanasilp, N. Dangniam, M.-A. Lemonde, and D. G. Angelakis, Expressibility and trainability of parametrized analog quantum systems for machine learning applications, Physical Review Research 2
2020
Earlier work this paper cites.
M. C. Caro and I. Datta, Pseudo-dimension of quantum circuits, Quantum Machine Intelligence 2
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre, Data re-uploading for a universal quantum classifier, Quantum 4
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics 16
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
R. Sweke, J.-P. Seifert, D. Hangleiter, and J. Eisert, On the Quantum versus Classical Learnability of Discrete Distributions, Quantum 5
2021
Earlier work this paper cites.
J. Kübler, S. Buchholz, and B. Schölkopf, The inductive bias of quantum kernels, Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
Y. Liu, S. Arunachalam, and K. Temme, A rigorous and robust quantum speed-up in supervised machine learning, Nature Physics , 1 (2021)
2021
Earlier work this paper cites.
E. Peters, J. Caldeira, A. Ho, S. Leichenauer, M. Mohseni, H. Neven, P. Spentzouris, D. Strain, and G. N. Perdue, Machine learning of high dimensional data on a noisy quantum processor, npj Quantum Information 7
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
P. Huembeli and A. Dauphin, Characterizing the loss landscape of variational quantum circuits, Quantum Science and Technology 6
2021
Cited alongside, same era.
C. O. Marrero, M. Kieferová, and N. Wiebe, Entanglement-induced barren plateaus, PRX Quantum 2
2021
Cited alongside, same era.
T. L. Patti, K. Najafi, X. Gao, and S. F. Yelin, Entanglement devised barren plateau mitigation, Physical Review Research 3
2021
Cited alongside, same era.
C. Zhao and X.-S. Gao, Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus, Quantum 5
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Closest in time.
Z. Holmes, K. Sharma, M. Cerezo, and P. J. Coles, Connecting ansatz expressibility to gradient magnitudes and barren plateaus, PRX Quantum 3
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
R. Shaydulin and S. M. Wild, Importance of kernel bandwidth in quantum machine learning, Physical Review A 106
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M. Cerezo and P. J. Coles, Higher order derivatives of quantum neural networks with barren plateaus, Quantum Science and Technology 6
2021
Cited alongside, same era.
A. Arrasmith, M. Cerezo, P. Czarnik, L. Cincio, and P. J. Coles, Effect of barren plateaus on gradient-free optimization, Quantum 5
2021
Cited alongside, same era.
D. Stilck França and R. Garcia-Patron, Limitations of optimization algorithms on noisy quantum devices, Nature Physics 17
2021
Cited alongside, same era.
2021
Cited alongside, same era.
L. Banchi, J. Pereira, and S. Pirandola, Generalization in quantum machine learning: A quantum information standpoint, PRX Quantum 2
2021
Cited alongside, same era.
A. Abbas, D. Sutter, C. Zoufal, A. Lucchi, A. Figalli, and S. Woerner, The power of quantum neural networks, Nature Computational Science 1
2021
Cited alongside, same era.
M. C. Caro, E. Gil-Fuster, J. J. Meyer, J. Eisert, and R. Sweke, Encoding-dependent generalization bounds for parametrized quantum circuits, Quantum 5
2021
Cited alongside, same era.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
K. Bu, D. E. Koh, L. Li, Q. Luo, and Y. Zhang, Statistical complexity of quantum circuits, Physical Review A 105
2022
Closest in time.
Y. Du, Z. Tu, X. Yuan, and D. Tao, Efficient measure for the expressivity of variational quantum algorithms, Physical Review Letters 128
2022
Closest in time.
H. Cai, Q. Ye, and D.-L. Deng, Sample complexity of learning parametric quantum circuits, Quantum Science and Technology 7
2022
Closest in time.
A. Arrasmith, Z. Holmes, M. Cerezo, and P. J. Coles, Equivalence of quantum barren plateaus to cost concentration and narrow gorges, Quantum Science and Technology 7
2022
Closest in time.
T. Hubregtsen, D. Wierichs, E. Gil-Fuster, P.-J. H. Derks, P. K. Faehrmann, and J. J. Meyer, Training quantum embedding kernels on near-term quantum computers, Physical Review A 106
2022
Closest in time.
B. Y. Gan, D. Leykam, and D. G. Angelakis, Fock state-enhanced expressivity of quantum machine learning models, EPJ Quantum Technology 9
2022
Closest in time.
J. Cotler, N. Hunter-Jones, and D. Ranard, Fluctuations of subsystem entropies at late times, Physical Review A 105
2022
Closest in time.
R. Takagi, S. Endo, S. Minagawa, and M. Gu, Fundamental limits of quantum error mitigation, npj Quantum Information 8
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
A. Elben, S. T. Flammia, H.-Y. Huang, R. Kueng, J. Preskill, B. Vermersch, and P. Zoller, The randomized measurement toolbox, Nature Review Physics 10.1038/s42254-022-00535-2 (2022)
2022
Closest in time.
J. Jäger and R. V. Krems, Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines, Nature Communications 14
2023
Closest in time.
Y. Wu, B. Wu, J. Wang, and X. Yuan, Quantum phase recognition via quantum kernel methods, Quantum 7
2023
Closest in time.
J. J. Meyer, M. Mularski, E. Gil-Fuster, A. A. Mele, F. Arzani, A. Wilms, and J. Eisert, Exploiting symmetry in variational quantum machine learning, PRX Quantum 4
2023
Closest in time.
A. Skolik, M. Cattelan, S. Yarkoni, T. Bäck, and V. Dunjko, Equivariant quantum circuits for learning on weighted graphs, npj Quantum Information 9
2023
Closest in time.
S. Jerbi, L. J. Fiderer, H. Poulsen Nautrup, J. M. Kübler, H. J. Briegel, and V. Dunjko, Quantum machine learning beyond kernel methods, Nature Communications 14
2023
Closest in time.
C. Gyurik, D. Vreumingen, van, and V. Dunjko, Structural risk minimization for quantum linear classifiers, Quantum 7
2023
Closest in time.
M. Larocca, N. Ju, D. García-Martín, P. J. Coles, and M. Cerezo, Theory of overparametrization in quantum neural networks, Nature Computational Science 3
2023
Closest in time.
L. Slattery, R. Shaydulin, S. Chakrabarti, M. Pistoia, S. Khairy, and S. M. Wild, Numerical evidence against advantage with quantum fidelity kernels on classical data, Phys. Rev. A 107
2023
Closest in time.