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Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.
M Neumark, “Spectral functions of a symmetric operator,” Izvestiya Rossiiskoi Akademii Nauk. Seriya Matematicheskaya 4
1940
Earlier work this paper cites.
Carl W Helstrom, “Quantum detection and estimation theory,” Journal of Statistical Physics 1
1969
Earlier work this paper cites.
Martin Riedmiller and Heinrich Braun, “A direct adaptive method for faster backpropagation learning: The rprop algorithm,” in Neural Networks, 1993., IEEE International Conference on (IEEE, 1993) pp. 586–591
1993
Earlier work this paper cites.
Christopher A Fuchs, “Distinguishability and accessible information in quantum theory,” arXiv preprint quant-ph/9601020 (1996)
1996
Earlier work this paper cites.
Charles H Bennett, Ethan Bernstein, Gilles Brassard, and Umesh Vazirani, “Strengths and weaknesses of quantum computing,” SIAM journal on Computing 26
1997
Earlier work this paper cites.
Christian Igel and Michael Hüsken, “Improving the Rprop learning algorithm,” in Proceedings of the second international ICSC symposium on neural computation (NC 2000) , Vol. 2000 (Citeseer, 2000) pp. 115–121
2000
Earlier work this paper cites.
Harry Buhrman, Richard Cleve, John Watrous, and Ronald De Wolf, “Quantum fingerprinting,” Physical Review Letters 87
2001
Earlier work this paper cites.
Sanjoy Dasgupta and Anupam Gupta, “An elementary proof of a theorem of Johnson and Lindenstrauss,” Random Structures & Algorithms 22
2003
Earlier work this paper cites.
Vivek V Shende, Igor L Markov, and Stephen S Bullock, “Minimal universal two-qubit controlled-not-based circuits,” Physical Review A 69
2004
Earlier work this paper cites.
Scott Aaronson, “The learnability of quantum states,” in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences , Vol. 463 (The Royal Society, 2007) pp. 3089–3114
2007
Earlier work this paper cites.
Koenraad MR Audenaert, John Calsamiglia, Ramón Muñoz-Tapia, Emilio Bagan, Ll Masanes, Antonio Acin, and Frank Verstraete, “Discriminating states: The quantum Chernoff bound,” Physical review letters 98
2007
Earlier work this paper cites.
Michael A. Nielsen and Isaac L. Chuang, Quantum Computation and Quantum Information: 10th Anniversary Edition , 10th ed. (Cambridge University Press, New York, NY, USA, 2011)
2011
Earlier work this paper cites.
Aram W Harrow, Ashley Montanaro, and Anthony J Short, “Limitations on quantum dimensionality reduction,” in International Colloquium on Automata, Languages, and Programming (Springer, 2011) pp. 86–97
2011
Earlier work this paper cites.
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost, “Quantum principal component analysis,” Nature Physics 10
2014
Cited alongside, same era.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 (2014) pp. 2672–2680
2014
Cited alongside, same era.
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature communications 5
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2018
Closest in time.
Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez, and Rupak Biswas, “Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers,” Quantum Science and Technology 3
2018
Closest in time.
Pierre-Luc Dallaire-Demers and Nathan Killoran, “Quantum generative adversarial networks,” Physical Review A 98
2018
Closest in time.
Seth Lloyd and Christian Weedbrook, “Quantum generative adversarial learning,” Physical Review Letters 121
2018
Closest in time.
John Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum 2
2018
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L Theis, A van den Oord, and M Bethge, “A note on the evaluation of generative models,” in International Conference on Learning Representations (ICLR 2016) (2016) pp. 1–10
2016
Cited alongside, same era.
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen, “Improved techniques for training gans,” in Advances in Neural Information Processing Systems 29 (2016) pp. 2234–2242
2016
Cited alongside, same era.
Roman Schmied, “Quantum state tomography of a single qubit: comparison of methods,” Journal of Modern Optics 63
2016
Cited alongside, same era.
Mária Kieferová and Nathan Wiebe, “Tomography and generative training with quantum Boltzmann machines,” Physical Review A 96
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
The Caltech Archives, “Richard Feynman’s blackboard at time of his death,” http://archives-dc.library.caltech.edu/islandora/object/ct1%3A483 (1988), Accessed on 2018-05-01
2018
Cited alongside, same era.
Mohammad H Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko, “Quantum Boltzmann machine,” Physical Review X 8
2018
Cited alongside, same era.
Closest in time.
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii, “Quantum circuit learning,” Physical Review A 98
2018
Closest in time.
Jin-Guo Liu and Lei Wang, “Differentiable learning of quantum circuit born machines,” Physical Review A 98
2018
Closest in time.
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven, “Barren plateaus in quantum neural network training landscapes,” Nature communications 9
2018
Closest in time.
Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G Green, and Simone Severini, “Hierarchical quantum classifiers,” npj Quantum Information 4
2018
Closest in time.
2018
Closest in time.
Lukasz Cincio, Yiğit Subaşı, Andrew T Sornborger, and Patrick J Coles, “Learning the quantum algorithm for state overlap,” New Journal of Physics 20
2018
Closest in time.
Ling Hu, Shu-Hao Wu, Weizhou Cai, Yuwei Ma, Xianghao Mu, Yuan Xu, Haiyan Wang, Yipu Song, Dong-Ling Deng, Chang-Ling Zou, et al. , “Quantum generative adversarial learning in a superconducting quantum circuit,” Science Advances 5
2019
Closest in time.