Fetching the paper…
Reading the bibliography…
We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks.
M. Born and V. Fock, “Beweis des Adiabatensatzes,” Zeitschrift fur Physik
1928
Earlier work this paper cites.
R. Bellman, “Dynamic programming and Lagrange multipliers,” Proceedings of the National Academy of Sciences
1956
Earlier work this paper cites.
M. Suzuki, “Relationship between d d -dimensional quantal spin systems and ( d d +1)-dimensional Ising systems equivalence, critical exponents and systematic approximants of the partition function and spin correlations,” Progr. Theor. Exp. Phys
1976
Earlier work this paper cites.
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, “A learning algorithm for Boltzmann machines,” Cogn. Sci
1985
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks
1989
Earlier work this paper cites.
R. S. Sutton, “Integrated architectures for learning, planning, and reacting based on approximating dynamic programming,” in In Proceedings of the Seventh International Conference on Machine Learning
1990
Earlier work this paper cites.
Anthropological Field Studies, Athena Scientific, 1996
D. Bertsekas and J. Tsitsiklis, Neuro-dynamic Programming · 1996
Earlier work this paper cites.
MIT Press, 1998
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction · 1998
Earlier work this paper cites.
I. Erev and A. E. Roth, “Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria,” Am. Econ. Rev
1998
Earlier work this paper cites.
T. Kadowaki and H. Nishimori, “Quantum annealing in the transverse Ising model,” Phys. Rev. E
1998
Earlier work this paper cites.
E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, “Quantum Computation by Adiabatic Evolution,” arXiv:quant-ph/0001106
2000
Earlier work this paper cites.
S. Singh, T. Jaakkola, M. L. Littman, and C. Szepesvári, “Convergence results for single-step on-policy reinforcement-learning algorithms,” Machine learning
2000
Earlier work this paper cites.
R. Martoňák, G. E. Santoro, and E. Tosatti, “Quantum annealing by the path-integral Monte Carlo method: The two-dimensional random Ising model,” Phys. Rev. B
2002
Earlier work this paper cites.
R. Martoňák, G. E. Santoro, and E. Tosatti, “Quantum annealing by the path-integral Monte Carlo method: The two-dimensional random Ising model,” Phys. Rev. B
2002
Earlier work this paper cites.
B. Sallans and G. E. Hinton, “Reinforcement learning with factored states and actions,” JMLR
2004
Earlier work this paper cites.
M. S. Sarandy and D. A. Lidar, “Adiabatic approximation in open quantum systems,” Phys. Rev. A
2005
Earlier work this paper cites.
S. Morita and H. Nishimori, “Convergence theorems for quantum annealing,” J. Phys. A: Mathematical and General
2006
Earlier work this paper cites.
S. Syafiie, F. Tadeo, and E. Martinez, “Model-free learning control of neutralization processes using reinforcement learning,” Engineering Applications of Artificial Intelligence
2007
Earlier work this paper cites.
N. Le Roux and Y. Bengio, “Representational power of restricted Boltzmann machines and deep belief networks,” Neural Computation
2008
Earlier work this paper cites.
D. Dong, C. Chen, H. Li, and T. J. Tarn, “Quantum reinforcement learning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
2008
Earlier work this paper cites.
R. Salakhutdinov and G. E. Hinton, “Deep Boltzmann Machines,” in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, AISTATS 2009, Clearwater Beach, Florida, USA, April 16-18, 2009
2009
Earlier work this paper cites.
Y. Matsuda, H. Nishimori, and H. G. Katzgraber, “Ground-state statistics from annealing algorithms: quantum versus classical approaches,” New. J. Phys
2009
Earlier work this paper cites.
Z. Sui, A. Gosavi, and L. Lin, “A reinforcement learning approach for inventory replenishment in vendor-managed inventory systems with consignment inventory,” Engineering Management Journal
2010
Cited alongside, same era.
R. Harris, M. W. Johnson, T. Lanting, A. J. Berkley, J. Johansson, P. Bunyk, E. Tolkacheva, E. Ladizinsky, N. Ladizinsky, T. Oh, F. Cioata, I. Perminov, P. Spear, C. Enderud, C. Rich, S. Uchaikin, M. C. Thom, E. M. Chapple, J. Wang, B. Wilson, M. H. S. Amin, N. Dickson, K. Karimi, B. Macready, C. J. S. Truncik, and G. Rose, “Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor,” Phys. Rev. B
2010
Cited alongside, same era.
G. Hinton, “A practical guide to training restricted Boltzmann machines,” Momentum
2010
Cited alongside, same era.
M. Otsuka, J. Yoshimoto, and K. Doya, “Free-energy-based reinforcement learning in a partially observable environment.,” ESANN 2010 proceedings, European Symposium on Artificial Neural Networks – Computational Intelligence and Machine Learning
B. Heim, T. F. Rønnow, S. V. Isakov, and M. Troyer, “Quantum versus classical annealing of Ising spin glasses,” Science
2015
Later among the works it cites.
2015
Later among the works it cites.
M. H. Amin, “Searching for quantum speedup in quasistatic quantum annealers,” Phys. Rev. A
2015
Later among the works it cites.
Springer-Verlag Berlin Heidelberg, 2015
S. M. Anthony Brabazon, Michael O’Neill, Natural Computing Algorithms · 2015
Later among the works it cites.
S. Elfwing, E. Uchibe, and K. Doya, “Scaled free-energy based reinforcement learning for robust and efficient learning in high-dimensional state spaces,” Value and Reward Based Learning in Neurobots
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2010
Cited alongside, same era.
P. M. Long and R. Servedio, “Restricted Boltzmann machines are hard to approximately evaluate or simulate,” in Proceedings of the 27th International Conference on Machine Learning
2010
Cited alongside, same era.
M. W. Johnson, M. H. S. Amin, S. Gildert, T. Lanting, F. Hamze, N. Dickson, R. Harris, A. J. Berkley, J. Johansson, P. Bunyk, E. M. Chapple, C. Enderud, J. P. Hilton, K. Karimi, E. Ladizinsky, N. Ladizinsky, T. Oh, I. Perminov, C. Rich, M. C. Thom, E. Tolkacheva, C. J. S. Truncik, S. Uchaikin, J. Wang, B. Wilson, and G. Rose, “Quantum annealing with manufactured spins,” Nature
2011
Cited alongside, same era.
T. Matsui, T. Goto, K. Izumi, and Y. Chen, “Compound reinforcement learning: theory and an application to finance,” in European Workshop on Reinforcement Learning
2011
Cited alongside, same era.
M. Denil and N. de Freitas, “Toward the implementation of a quantum RBM,” in NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop
2011
Cited alongside, same era.
J. Duchi, E. Hazan, and Y. Singer, “Adaptive subgradient methods for online learning and stochastic optimization,” JMLR
2011
Cited alongside, same era.
F. Abtahi and I. Fasel, “Deep belief nets as function approximators for reinforcement learning,” Frontiers in Computational Neuroscience
2011
Cited alongside, same era.
J. E. Avron, M. Fraas, G. M. Graf, and P. Grech, “Adiabatic theorems for generators of contracting evolutions,” Commun. Math. Phys
2012
Cited alongside, same era.
T. Albash, S. Boixo, D. A. Lidar, and P. Zanardi, “Quantum adiabatic Markovian master equations,” New J. Phys
2012
Cited alongside, same era.
2015
Later among the works it cites.
2016
Closest in time.
L. C. Venuti, T. Albash, D. A. Lidar, and P. Zanardi, “Adiabaticity in open quantum systems,” Phys. Rev. A
2016
Closest in time.
2016
Closest in time.
M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz, “Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning,” Phys. Rev. A
2016
Closest in time.
V. Dunjko, J. M. Taylor, and H. J. Briegel, “Quantum-enhanced machine learning,” Phys. Rev. Lett
2016
Closest in time.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum deep learning,” Quantum Inf. Comput
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
L. T. Brady and W. van Dam, “Quantum Monte Carlo simulations of tunneling in quantum adiabatic optimization,” Phys. Rev. A
2016
Closest in time.
2016
Closest in time.
S. Yuksel, “Control of stochastic systems.” Course lecture notes, Queen’s University (Kingston, ON Canada), Retrieved in May, 2016
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
2016
Closest in time.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al., “Mastering the game of go with deep neural networks and tree search,” Nature
2016
Closest in time.
L. C. Venuti, T. Albash, M. Marvian, D. Lidar, and P. Zanardi, “Relaxation versus adiabatic quantum steady-state preparation,” Phys. Rev. A
2017
Closest in time.