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Neuroevolution, a field that draws inspiration from the evolution of brains in nature, harnesses evolutionary algorithms to construct artificial neural networks.
M. Ostaszewski, E. Grant, and M. Benedetti, Quantum circuit structure learning, arXiv:1905.09692
1905
Earlier work this paper cites.
1906
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1910
Earlier work this paper cites.
W. Wolberg, N. Street, and O. Mangasarian, UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set (1992)
1992
Earlier work this paper cites.
Y. LeCun, C. Cortes, and C. Burges, MNIST handwritten digit database (1998)
1998
Earlier work this paper cites.
2001
Earlier work this paper cites.
K. O. Stanley and R. Miikkulainen, Evolving neural networks through augmenting topologies, Evol. Comput. 10
2002
Earlier work this paper cites.
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum Algorithm for Linear Systems of Equations, Phys. Rev. Lett. 103
2009
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, 2010)
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
P. Smacchia, L. Amico, P. Facchi, R. Fazio, G. Florio, S. Pascazio, and V. Vedral, Statistical mechanics of the cluster Ising model, Phys. Rev. A 84
2011
Earlier work this paper cites.
M. Pirhooshyaran and T. Terlaky, Quantum Circuit Design Search, arXiv:2012.04046
2012
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, Quantum principal component analysis, Nat. Phys. 10
2014
Earlier work this paper cites.
P. Rebentrost, M. Mohseni, and S. Lloyd, Quantum Support Vector Machine for Big Data Classification, Phys. Rev. Lett. 113
2014
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature 521
2015
Earlier work this paper cites.
M. I. Jordan and T. M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science 349
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, Mastering the game of Go with deep neural networks and tree search, Nature 529
2016
Earlier work this paper cites.
L. Wang, Discovering phase transitions with unsupervised learning, Phys. Rev. B 94
2016
Earlier work this paper cites.
V. Dunjko, J. M. Taylor, and H. J. Briegel, Quantum-Enhanced Machine Learning, Phys. Rev. Lett. 117
2016
Earlier work this paper cites.
N. Deo, Graph Theory with Applications to Engineering and Computer Science , 1st ed. (Dover Publications, Mineola, New York, 2016)
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, Cambridge, 2016)
2016
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.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis, Mastering the game of Go without human knowledge, Nature 550
2017
Earlier work this paper cites.
G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355
2017
Cited alongside, same era.
Y. Zhang and E.-A. Kim, Quantum Loop Topography for Machine Learning, Phys. Rev. Lett. 118
2017
Cited alongside, same era.
J. Carrasquilla and R. G. Melko, Machine learning phases of matter, Nat. Phys. 13
2017
Cited alongside, same era.
E. P. L. van Nieuwenburg, Y.-H. Liu, and S. D. Huber, Learning phase transitions by confusion, Nat. Phys. 13
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, Sci. Rep. 7
2017
Cited alongside, same era.
S. Das Sarma, D.-L. Deng, and L.-M. Duan, Machine learning meets quantum physics, Phys. Today 72
2019
Later among the works it cites.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Machine learning and the physical sciences, Rev. Mod. Phys. 91
2019
Later among the works it cites.
J. Carrasquilla, G. Torlai, R. G. Melko, and L. Aolita, Reconstructing quantum states with generative models, Nat. Mach. Intell. 1
2019
Later among the works it cites.
Y. Zhang, A. Mesaros, K. Fujita, S. D. Edkins, M. H. Hamidian, K. Ch’ng, H. Eisaki, S. Uchida, J. C. S. Davis, E. Khatami, and E.-A. Kim, Machine learning in electronic-quantum-matter imaging experiments, Nature 570
2019
Later among the works it cites.
W. Lian, S.-T. Wang, S. Lu, Y. Huang, F. Wang, X. Yuan, W. Zhang, X. Ouyang, X. Wang, X. Huang, L. He, X. Chang, D.-L. Deng, and L. Duan, Machine Learning Topological Phases with a Solid-State Quantum Simulator, Phys. Rev. Lett. 122
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2017
Cited alongside, same era.
Y. Zhang, R. G. Melko, and E.-A. Kim, Machine learning ℤ 2 {\mathbb{Z}}_{2} quantum spin liquids with quasiparticle statistics, Phys. Rev. B 96
2017
Cited alongside, same era.
S. J. Wetzel, Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders, Phys. Rev. E 96
2017
Cited alongside, same era.
W. Hu, R. R. P. Singh, and R. T. Scalettar, Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination, Phys. Rev. E 95
2017
Cited alongside, same era.
M. Schuld, M. Fingerhuth, and F. Petruccione, Implementing a distance-based classifier with a quantum interference circuit, EPL Europhys. Lett. 119
2017
Cited alongside, same era.
K. H. Wan, O. Dahlsten, H. Kristjánsson, R. Gardner, and M. S. Kim, Quantum generalisation of feedforward neural networks, Npj Quantum Inf. 3
2017
Cited alongside, same era.
R. Li, U. Alvarez-Rodriguez, L. Lamata, and E. Solano, Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience, Quantum Meas. Quantum Metrol. 4
2017
Cited alongside, same era.
2019
Later among the works it cites.
L. Hu, S.-H. Wu, W. Cai, Y. Ma, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou, and L. Sun, Quantum generative adversarial learning in a superconducting quantum circuit, Sci. Adv. 5
2019
Later among the works it cites.
M. Schuld and N. Killoran, Quantum Machine Learning in Feature Hilbert Spaces, Phys. Rev. Lett. 122
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 567
2019
Later among the works it cites.
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, L. Egan, O. Perdomo, and C. Monroe, Training of quantum circuits on a hybrid quantum computer, Sci. Adv. 5
2019
Later among the works it cites.
I. Cong, S. Choi, and M. D. Lukin, Quantum convolutional neural networks, Nat. Phys. 15
2019
Later among the works it cites.
F. Tacchino, C. Macchiavello, D. Gerace, and D. Bajoni, An artificial neuron implemented on an actual quantum processor, Npj Quantum Inf. 5
2019
Later among the works it cites.
C. Kokail, C. Maier, R. van Bijnen, T. Brydges, M. K. Joshi, P. Jurcevic, C. A. Muschik, P. Silvi, R. Blatt, C. F. Roos, and P. Zoller, Self-verifying variational quantum simulation of lattice models, Nature 569
2019
Later among the works it cites.
J.-G. Liu, Y.-H. Zhang, Y. Wan, and L. Wang, Variational quantum eigensolver with fewer qubits, Phys. Rev. Research 1
2019
Later among the works it cites.
D. Wang, O. Higgott, and S. Brierley, Accelerated Variational Quantum Eigensolver, Phys. Rev. Lett. 122
2019
Later among the works it cites.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Regularized evolution for image classifier architecture search, in Proceedings of the Aaai Conference on Artificial Intelligence , Vol. 33 (2019) pp. 4780–4789
2019
Later among the works it cites.
K. O. Stanley, J. Clune, J. Lehman, and R. Miikkulainen, Designing neural networks through neuroevolution, Nat. Mach. Intell. 1
2019
Later among the works it cites.
E. Grant, L. Wossnig, M. Ostaszewski, and M. Benedetti, An initialization strategy for addressing barren plateaus in parametrized quantum circuits, Quantum 3
2019
Later among the works it cites.
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis, Improved protein structure prediction using potentials from deep learning, Nature 577
2020
Closest in time.
Y.-H. Zhang, P.-L. Zheng, Y. Zhang, and D.-L. Deng, Topological Quantum Compiling with Reinforcement Learning, Phys. Rev. Lett. 125
2020
Closest in time.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, Circuit-centric quantum classifiers, Phys. Rev. A 101
2020
Closest in time.
B. Coyle, D. Mills, V. Danos, and E. Kashefi, The Born supremacy: Quantum advantage and training of an Ising Born machine, Npj Quantum Inf. 6
2020
Closest in time.
L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. D. Lukin, Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices, Phys. Rev. X 10
2020
Closest in time.
L. Li, M. Fan, M. Coram, P. Riley, and S. Leichenauer, Quantum optimization with a novel Gibbs objective function and ansatz architecture search, Phys. Rev. Research 2
2020
Closest in time.
See Supplemental Material at [URL will be inserted by publisher] for details on the graph-encoding method and the MQNE algorithm, and more numerical results to demonstrate the performance of the proposed scheme, which include Refs. Bezanson et al. 2017 ; Luo et al. 2020
2020
Closest in time.
S. Lu, L.-M. Duan, and D.-L. Deng, Quantum adversarial machine learning, Phys. Rev. Research 2
2020
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N. Liu and P. Wittek, Vulnerability of quantum classification to adversarial perturbations, Phys. Rev. A 101
2020
Closest in time.
2020
Closest in time.
S. Huang, X. Li, Z.-Q. Cheng, Z. Zhang, and A. Hauptmann, GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning, in Proceedings of the 26th ACM International Conference on Multimedia , MM ’18 (Association for Computing Machinery, New York, NY, USA, 2018) pp. 2049–2057
2057
Closest in time.