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Neural network-based algorithms have garnered considerable attention in condensed matter physics for their ability to learn complex patterns from very high dimensional data sets towards classifying complex long-range patterns of entanglement and correlations in many-body quantum systems.
F. Vatan and C. Williams, “Optimal quantum circuits for general two-qubit gates,” Physical Review A
2004
Earlier work this paper cites.
2009
Earlier work this paper cites.
U. Schollwöck, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics
2011
Earlier work this paper cites.
P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Phys. Rev. Lett
2014
Earlier work this paper cites.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics
2016
Earlier work this paper cites.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature
2017
Earlier work this paper cites.
I. H. Kim and B. Swingle, “Robust entanglement renormalization on a noisy quantum computer,” 2017
2017
Earlier work this paper cites.
K. H. Wan, O. Dahlsten, H. Kristjánsson, R. Gardner, and M. S. Kim, “Quantum generalisation of feedforward neural networks,” npj Quantum Information
2017
Earlier work this paper cites.
J. Romero, J. P. Olson, and A. Aspuru-Guzik, “Quantum autoencoders for efficient compression of quantum data,” Quantum Science and Technology
2017
Earlier work this paper cites.
G. Carleo and M. Troyer, “Solving the quantum many-body problem with artificial neural networks,” Science
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
E. Farhi, J. Goldstone, S. Gutmann, and H. Neven, “Quantum algorithms for fixed qubit architectures,” 2017
2017
Cited alongside, same era.
P. Broecker, J. Carrasquilla, R. G. Melko, and S. Trebst, “Machine learning quantum phases of matter beyond the fermion sign problem,” Scientific Reports
2017
Cited alongside, same era.
D.-L. Deng, X. Li, and S. Das Sarma, “Quantum Entanglement in Neural Network States,” Physical Review X
2017
Cited alongside, same era.
J. Preskill, “Quantum Computing in the NISQ era and beyond,” arXiv e-prints
2018
Cited alongside, same era.
V. Dunjko and H. J. Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Reports on Progress in Physics
2018
Cited alongside, same era.
W. Huggins, P. Patil, B. Mitchell, K. B. Whaley, and E. M. Stoudenmire, “Towards quantum machine learning with tensor networks,” Quantum Science and Technology
2019
Later among the works it cites.
M. Schuld and N. Killoran, “Quantum machine learning in feature hilbert spaces,” Phys. Rev. Lett
2019
Later among the works it 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 (London)
2019
Later among the works it cites.
I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nature Physics
2019
Later among the works it cites.
2019
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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
2018
Cited alongside, same era.
E. Farhi and H. Neven, “Classification with quantum neural networks on near term processors,” 2018
2018
Cited alongside, same era.
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine learning for molecular and materials science,” Nature
2018
Cited alongside, same era.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,” Nature Communications
2018
Cited alongside, same era.
C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage, “Trapped-ion quantum computing: Progress and challenges,” Applied Physics Reviews
2019
Cited alongside, same era.
2019
Cited alongside, same era.
G. Verdon, J. Marks, S. Nanda, S. Leichenauer, and J. Hidary, “Quantum hamiltonian-based models and the variational quantum thermalizer algorithm,” 2019
2019
Cited alongside, same era.
Later among the works it cites.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” Phys. Rev. A
2020
Closest in time.
L. Franken and B. Georgiev, “Explorations in quantum neural networks with intermediate measurements,” in Proceedings of ESANN
2020
Closest in time.
2020
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2020
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“Docs and resources,” 2020
2020
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2020
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2020
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