Fetching the paper…
Reading the bibliography…
Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability.
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski, “A learning algorithm for boltzmann machines,” Cognitive science 9
1985
Earlier work this paper cites.
Geoffrey E. Hinton, Peter Dayan, Brendan J. Frey, and Radford M. Neal, “The wake-sleep algorithm for unsupervised neural networks,” Science 268
1995
Earlier work this paper cites.
Peter Dayan, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel, “The helmholtz machine,” Neural computation 7
1995
Earlier work this paper cites.
Seth Lloyd and Samuel L Braunstein, “Quantum computation over continuous variables,” Physical Review Letters 82
1999
Earlier work this paper cites.
Laurent Younes, “On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates,” Stochastics: An International Journal of Probability and Stochastic Processes 65
1999
Earlier work this paper cites.
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh, “A fast learning algorithm for deep belief nets,” Neural computation 18
2006
Earlier work this paper cites.
Harmut Neven, Vasil S Denchev, Marshall Drew-Brook, Jiayong Zhang, William G Macready, and Geordie Rose, “Binary classification using hardware implementation of quantum annealing,” in Demonstrations at NIPS-09, 24th Annual Conference on Neural Information Processing Systems (2009) pp. 1–17
2009
Earlier work this paper cites.
Yoshua Bengio et al. , “Learning deep architectures for ai,” Foundations and trend in Machine Learning 2
2009
Earlier work this paper cites.
Ruslan Salakhutdinov and Geoffrey Hinton, “Deep boltzmann machines,” in Artificial Intelligence and Statistics (2009) pp. 448–455
2009
Earlier work this paper cites.
Zhengbing Bian, Fabian Chudak, William G Macready, and Geordie Rose, The Ising model: teaching an old problem new tricks , Tech. Rep. (D-Wave Systems, 2010)
2010
Earlier work this paper cites.
Ruslan Salakhutdinov and Hugo Larochelle, “Efficient learning of deep boltzmann machines,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (2010) pp. 693–700
2010
Earlier work this paper cites.
Misha Denil and Nando De Freitas, “Toward the implementation of a quantum RBM,” NIPS Deep Learning and Unsupervised Feature Learning Workshop (2011)
2011
Earlier work this paper cites.
Nathan Wiebe, Daniel Braun, and Seth Lloyd, “Quantum algorithm for data fitting,” Physical review letters 109
2012
Earlier work this paper cites.
Kristen L. Pudenz and Daniel A. Lidar, “Quantum adiabatic machine learning,” Quantum Information Processing 12
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd, “Quantum support vector machine for big data classification,” Phys. Rev. Lett. 113
2014
Earlier work this paper cites.
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost, “Quantum principal component analysis,” Nature Physics 10
2014
Earlier work this paper cites.
Jörg Bornschein and Yoshua Bengio, “Reweighted wake-sleep,” arXiv preprint arXiv:1406.2751 (2014)
2014
Cited alongside, same era.
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 (2014) pp. 2672–2680
2014
Cited alongside, same era.
2015
Cited alongside, same era.
Ian Goodfellow Yoshua Bengio and Aaron Courville, “Deep learning,” (2016), mIT Press
2016
Later among the works it cites.
Jorg Bornschein, Samira Shabanian, Asja Fischer, and Yoshua Bengio, “Bidirectional helmholtz machines,” in International Conference on Machine Learning (2016) pp. 2511–2519
2016
Later among the works it cites.
Thomas E Potok, Catherine D Schuman, Steven R Young, Robert M Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, and Gangotree Chakma, “A study of complex deep learning networks on high performance, neuromorphic, and quantum computers,” in Proceedings of the Workshop on Machine Learning in High Performance Computing Environments (IEEE Press, 2016) pp. 47–55
2016
Later among the works it cites.
Guoming Wang, “Quantum algorithm for linear regression,” Physical Review A 96
2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
Scott Aaronson, “Read the fine print,” Nature Physics 11
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “An introduction to quantum machine learning,” Contemporary Physics 56
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Wolfgang Lechner, Philipp Hauke, and Peter Zoller, “A quantum annealing architecture with all-to-all connectivity from local interactions,” Science advances 1
2015
Cited alongside, same era.
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione, “Prediction by linear regression on a quantum computer,” Physical Review A 94
2016
Cited alongside, same era.
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, and Alejandro Perdomo-Ortiz, “Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning,” Phys. Rev. A 94
2016
Cited alongside, same era.
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, and Alejandro Perdomo-Ortiz, “Quantum-assisted learning of hardware-embedded probabilistic graphical models,” Phys. Rev. X 7
2017
Closest in time.
2017
Closest in time.
Mária Kieferová and Nathan Wiebe, “Tomography and generative training with quantum boltzmann machines,” Phys. Rev. A 96
2017
Closest in time.
Peter Wittek and Christian Gogolin, “Quantum enhanced inference in markov logic networks,” Scientific Reports 7
2017
Closest in time.
Jonathan Romero, Jonathan P Olson, and Alan Aspuru-Guzik, “Quantum autoencoders for efficient compression of quantum data,” Quantum Sci. Technol. 2
2017
Closest in time.
Lucas Lamata, “Basic protocols in quantum reinforcement learning with superconducting circuits,” Scientific Reports 7
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
Hoi-Kwan Lau, Raphael Pooser, George Siopsis, and Christian Weedbrook, “Quantum machine learning over infinite dimensions,” Physical Review Letters 118
2017
Closest in time.
2017
Closest in time.
“A sub-sampled version of the mnist dataset,” https://github.com/marybigday/stat665-1/tree/master/data (Accessed: August 2017)
2017
Closest in time.
2018
Closest in time.