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Machine learning is frequently listed among the most promising applications for quantum computing.
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Cited alongside, same era.
C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, and L. Wossnig, Quantum machine learning: a classical perspective, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474
2018
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A. Perdomo-Ortiz, M. Benedetti, J. Realpe-Gómez, and R. Biswas, Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers, Quantum Science and Technology 3
2018
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M. H. Amin, E. Andriyash, J. Rolfe, B. Kulchytskyy, and R. Melko, Quantum Boltzmann machine, Physical Review X 8
2018
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D. Soudry, E. Hoffer, M. S. Nacson, S. Gunasekar, and N. Srebro, The implicit bias of gradient descent on separable data, The Journal of Machine Learning Research 19
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J.-G. Liu and L. Wang, Differentiable learning of quantum circuit born machines, Physical Review A 98
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2020
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M. Schuld and F. Petruccione, Machine Learning with Quantum Computers (Springer, 2021)
2021
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2021
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C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, Understanding deep learning (still) requires rethinking generalization, Communications of the ACM 64
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Y. Wu, J. Yao, P. Zhang, and H. Zhai, Expressivity of quantum neural networks, Physical Review Research 3
2021
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M. Schuld, R. Sweke, and J. J. Meyer, Effect of data encoding on the expressive power of variational quantum-machine-learning models, Physical Review A 103
2021
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A. Abbas, D. Sutter, C. Zoufal, A. Lucchi, A. Figalli, and S. Woerner, The power of quantum neural networks, Nature Computational Science 1
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R. Sweke, J.-P. Seifert, D. Hangleiter, and J. Eisert, On the quantum versus classical learnability of discrete distributions, Quantum 5
2021
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2021
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J. Kübler, S. Buchholz, and B. Schölkopf, The inductive bias of quantum kernels, Advances in Neural Information Processing Systems 34
2021
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R. Babbush, J. R. McClean, M. Newman, C. Gidney, S. Boixo, and H. Neven, Focus beyond quadratic speedups for error-corrected quantum advantage, PRX Quantum 2
2021
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P. Selig, N. Murphy, A. Sundareswaran, D. Redmond, and S. Caton, A case for noisy shallow gate-based circuits in quantum machine learning, in 2021 International Conference on Rebooting Computing (ICRC) (IEEE, 2021) pp. 24–34
2021
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2021
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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
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2021
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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
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L. Banchi and G. E. Crooks, Measuring analytic gradients of general quantum evolution with the stochastic parameter shift rule, Quantum 5
2021
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J. S. Kottmann, A. Anand, and A. Aspuru-Guzik, A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers, Chemical Science 12
2021
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A. Mari, T. R. Bromley, and N. Killoran, Estimating the gradient and higher-order derivatives on quantum hardware, Physical Review A 103
2021
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2021
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2021
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M. Ostaszewski, E. Grant, and M. Benedetti, Structure optimization for parameterized quantum circuits, Quantum 5
2021
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2022
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