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Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data.
1909
Earlier work this paper cites.
Frank Rosenblatt, The perceptron, a perceiving and recognizing automaton Project Para (Cornell Aeronautical Laboratory, 1957)
1957
Earlier work this paper cites.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams, “Learning representations by back-propagating errors,” Nature (London) 323
1986
Earlier work this paper cites.
Simon Haykin, Neural networks: a comprehensive foundation (Prentice Hall PTR, NJ, 1994)
1994
Earlier work this paper cites.
MV Altaisky, “Quantum neural network,” arXiv preprint quant-ph/0107012 (2001)
2001
Earlier work this paper cites.
2004
Earlier work this paper cites.
Noriaki Kouda, Nobuyuki Matsui, Haruhiko Nishimura, and Ferdinand Peper, “Qubit neural network and its learning efficiency,” Neural Computing & Applications 14
2005
Earlier work this paper cites.
Christoph Dankert, Richard Cleve, Joseph Emerson, and Etera Livine, “Exact and approximate unitary 2-designs and their application to fidelity estimation,” Physical Review A 80
2009
Earlier work this paper cites.
2013
Earlier work this paper cites.
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “The quest for a quantum neural network,” Quantum Information Processing 13
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,” Nature Communications 5
2014
Earlier work this paper cites.
Michael Siomau, “A quantum model for autonomous learning automata,” Quantum information processing 13
2014
Earlier work this paper cites.
Michael A Nielsen, Neural networks and deep learning , Vol. 2018 (Determination press San Francisco, CA, USA:, 2015)
2015
Earlier work this paper cites.
Bela Bauer, Dave Wecker, Andrew J Millis, Matthew B Hastings, and Matthias Troyer, “Hybrid quantum-classical approach to correlated materials,” Physical Review X 6
2016
Earlier work this paper cites.
Jarrod R McClean, Jonathan Romero, Ryan Babbush, and Alán Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics 18
2016
Earlier work this paper cites.
Fernando GSL Brandao, Aram W Harrow, and Michał Horodecki, “Local random quantum circuits are approximate polynomial-designs,” Communications in Mathematical Physics 346
2016
Earlier work this paper cites.
Marvin Minsky and Seymour A Papert, Perceptrons: An introduction to computational geometry (MIT press, Cambridge, MA, 2017)
2017
Earlier work this paper cites.
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549
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 2
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,” Nature 549
2017
Earlier work this paper cites.
Neena Aloysius and M Geetha, “A review on deep convolutional neural networks,” in 2017 International Conference on Communication and Signal Processing (ICCSP) (IEEE, 2017) pp. 0588–0592
2017
Earlier work this paper cites.
Zbigniew Puchała and Jaroslaw Adam Miszczak, “Symbolic integration with respect to the haar measure on the unitary groups,” Bulletin of the Polish Academy of Sciences Technical Sciences 65
2017
Earlier work this paper cites.
J. Preskill, “Quantum computing in the NISQ era and beyond,” Quantum 2
2018
Cited alongside, same era.
Vedran Dunjko and Hans J Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Reports on Progress in Physics 81
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, and Leonard Wossnig, “Quantum machine learning: a classical perspective,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti, “An initialization strategy for addressing barren plateaus in parametrized quantum circuits,” Quantum 3
2019
Later among the works it cites.
2019
Later among the works it cites.
Motohisa Fukuda, Robert König, and Ion Nechita, “RTNI—a symbolic integrator for haar-random tensor networks,” Journal of Physics A: Mathematical and Theoretical 52
2019
Later among the works it cites.
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J Osborne, Robert Salzmann, Daniel Scheiermann, and Ramona Wolf, “Training deep quantum neural networks,” Nature Communications 11
2020
Closest in time.
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2018
Cited alongside, same era.
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
Cited alongside, same era.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Phys. Rev. A 98
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Nathan Killoran, Thomas R Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, and Seth Lloyd, “Continuous-variable quantum neural networks,” Physical Review Research 1
2019
Cited alongside, same era.
Iris Cong, Soonwon Choi, and Mikhail D Lukin, “Quantum convolutional neural networks,” Nature Physics 15
2019
Cited alongside, same era.
Zhih-Ahn Jia, Biao Yi, Rui Zhai, Yu-Chun Wu, Guang-Can Guo, and Guo-Ping Guo, “Quantum neural network states: A brief review of methods and applications,” Advanced Quantum Technologies 2
2019
Cited alongside, same era.
A. Arrasmith, L. Cincio, A. T. Sornborger, W. H. Zurek, and P. J. Coles, “Variational consistent histories as a hybrid algorithm for quantum foundations,” Nature communications 10
2019
Cited alongside, same era.
Dmytro Bondarenko and Polina Feldmann, “Quantum autoencoders to denoise quantum data,” Physical Review Letters 124
2020
Closest in time.
2020
Closest in time.
Kunal Sharma, Sumeet Khatri, Marco Cerezo, and Patrick J Coles, “Noise resilience of variational quantum compiling,” New Journal of Physics 22
2020
Closest in time.
Hsin-Yuan Huang, Richard Kueng, and John Preskill, “Predicting many properties of a quantum system from very few measurements,” Nature Physics 16
2020
Closest in time.
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao, “The expressive power of parameterized quantum circuits,” Phys. Rev. Research 2
2020
Closest in time.
James Stokes, Josh Izaac, Nathan Killoran, and Giuseppe Carleo, “Quantum natural gradient,” Quantum 4
2020
Closest in time.
Jonas M. Kübler, Andrew Arrasmith, Lukasz Cincio, and Patrick J. Coles, “An Adaptive Optimizer for Measurement-Frugal Variational Algorithms,” Quantum 4
2020
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2020
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2020
Closest in time.
Marco Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J Coles, “Cost function dependent barren plateaus in shallow parametrized quantum circuits,” Nature communications 12
2021
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Carlos Ortiz Marrero, Mária Kieferová, and Nathan Wiebe, “Entanglement induced barren plateaus,” PRX Quantum 2
2021
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Tyler Volkoff and Patrick J Coles, “Large gradients via correlation in random parameterized quantum circuits,” Quantum Science and Technology 6
2021
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Adrien Bolens and Markus Heyl, “Reinforcement learning for digital quantum simulation,” Phys. Rev. Lett. 127
2021
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Arthur Pesah, M Cerezo, Samson Wang, Tyler Volkoff, Andrew T Sornborger, and Patrick J Coles, “Absence of barren plateaus in quantum convolutional neural networks,” Phys. Rev. X 11
2021
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Chen Zhao and Xiao-Shan Gao, “Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus,” Quantum 5
2021
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Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, and Stefan Woerner, “The power of quantum neural networks,” Nature Computational Science 1
2021
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Taylor L Patti, Khadijeh Najafi, Xun Gao, and Susanne F Yelin, “Entanglement devised barren plateau mitigation,” Physical Review Research 3
2021
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Kunal Sharma, M Cerezo, Zoë Holmes, Lukasz Cincio, Andrew Sornborger, and Patrick J Coles, “Reformulation of the No-Free-Lunch theorem for entangled data sets,” Phys. Rev. Lett. 128
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