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Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage.
L. Adleman, Two theorems on random polynomial time, in 19th Annual Symposium on Foundations of Computer Science (sfcs 1978) (IEEE Computer Society, 1978) pp. 75–83
1978
Earlier work this paper cites.
W. Alexi, B. Chor, O. Goldreich, and C. P. Schnorr, Rsa and rabin functions: Certain parts are as hard as the whole, SIAM Journal on Computing 17
1988
Earlier work this paper cites.
M. J. Kearns and U. Vazirani, An introduction to computational learning theory (MIT press, 1994)
1994
Earlier work this paper cites.
R. Canetti, G. Even, and O. Goldreich, Lower bounds for sampling algorithms for estimating the average, Information Processing Letters 53
1995
Earlier work this paper cites.
L. K. Grover, A fast quantum mechanical algorithm for database search, in Proceedings of the twenty-eighth annual ACM symposium on Theory of computing (1996) pp. 212–219
1996
Earlier work this paper cites.
P. W. Shor, Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM review 41
1999
Earlier work this paper cites.
P. Dagum, R. Karp, M. Luby, and S. Ross, An optimal algorithm for monte carlo estimation, SIAM Journal on computing 29
2000
Earlier work this paper cites.
M. Anthony and P. L. Bartlett, Function learning from interpolation, Combinatorics, Probability and Computing 9
2000
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, 2000)
2000
Earlier work this paper cites.
A. Ambainis, A. Nayak, A. Ta-Shma, and U. Vazirani, Dense quantum coding and quantum finite automata, Journal of the ACM (JACM) 49
2002
Earlier work this paper cites.
R. A. Servedio and S. J. Gortler, Equivalences and separations between quantum and classical learnability, SIAM Journal on Computing 33
2004
Earlier work this paper cites.
S. Aaronson, The learnability of quantum states, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463
2007
Earlier work this paper cites.
M. Cramer, M. B. Plenio, S. T. Flammia, R. Somma, D. Gross, S. D. Bartlett, O. Landon-Cardinal, D. Poulin, and Y.-K. Liu, Efficient quantum state tomography, Nature communications 1
2010
Earlier work this paper cites.
S. Aaronson and A. Arkhipov, The computational complexity of linear optics, in Proceedings of the forty-third annual ACM symposium on Theory of computing (2011) pp. 333–342
2011
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.
L. Wang and X. Ma, Bounds of graph energy in terms of vertex cover number, Linear Algebra and its Applications 517
2017
Earlier work this paper cites.
V. Dunjko and H. J. Briegel, Machine learning & artificial intelligence in the quantum domain: a review of recent progress, Reports on Progress in Physics 81
2018
Earlier work this paper cites.
M. Schuld and F. Petruccione, Supervised learning with quantum computers , Vol. 17 (Springer, 2018)
2018
Cited alongside, same era.
J.-G. Liu and L. Wang, Differentiable learning of quantum circuit born machines, Physical Review A 98
2018
Cited alongside, same era.
M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Science and Technology 4
2019
Cited alongside, same era.
M. Schuld and N. Killoran, Quantum machine learning in feature hilbert spaces, Physical review letters 122
2019
Cited alongside, same era.
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
Cited alongside, same era.
H.-Y. Huang, M. Broughton, M. Mohseni, R. Babbush, S. Boixo, H. Neven, and J. R. McClean, Power of data in quantum machine learning, Nature Communications 12
2021
Later among the works it cites.
K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, et al. , Noisy intermediate-scale quantum algorithms, Reviews of Modern Physics 94
2022
Later among the works it cites.
A. Skolik, S. Jerbi, and V. Dunjko, Quantum agents in the gym: a variational quantum algorithm for deep q-learning, Quantum 6
2022
Later among the works it cites.
2022
Later among the works it cites.
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D. Zhu, N. M. Linke, M. Benedetti, K. A. Landsman, N. H. Nguyen, C. H. Alderete, A. Perdomo-Ortiz, N. Korda, A. Garfoot, C. Brecque, et al. , Training of quantum circuits on a hybrid quantum computer, Science advances 5
2019
Cited alongside, same era.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, Circuit-centric quantum classifiers, Physical Review A 101
2020
Cited alongside, same era.
H.-Y. Huang, R. Kueng, and J. Preskill, Predicting many properties of a quantum system from very few measurements, Nature Physics 16
2020
Cited alongside, same era.
M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al. , Variational quantum algorithms, Nature Reviews Physics 3
2021
Cited alongside, same era.
S. Jerbi, C. Gyurik, S. Marshall, H. Briegel, and V. Dunjko, Parametrized quantum policies for reinforcement learning, Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
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
Cited alongside, same era.
Y. Liu, S. Arunachalam, and K. Temme, A rigorous and robust quantum speed-up in supervised machine learning, Nature Physics , 1 (2021)
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
H.-Y. Huang, M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskill, et al. , Quantum advantage in learning from experiments, Science 376
2022
Later among the works it cites.
T. Haug, C. N. Self, and M. Kim, Quantum machine learning of large datasets using randomized measurements, Machine Learning: Science and Technology 4
2023
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2023
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F. J. Schreiber, J. Eisert, and J. J. Meyer, Classical surrogates for quantum learning models, Physical Review Letters 131
2023
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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
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K. Wan, W. J. Huggins, J. Lee, and R. Babbush, Matchgate shadows for fermionic quantum simulation, Communications in Mathematical Physics , 1 (2023)
2023
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2023
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C. Gyurik, V. Dunjko, et al. , Structural risk minimization for quantum linear classifiers, Quantum 7
2023
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B. Casas and A. Cervera-Lierta, Multidimensional fourier series with quantum circuits, Physical Review A 107
2023
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L. Leone, S. F. Oliviero, and A. Hamma, Nonstabilizerness determining the hardness of direct fidelity estimation, Physical Review A 107
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