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Machine learning can help us in solving problems in the context big data analysis and classification, as well as in playing complex games such as Go.
A. Jamiołkowski, “Linear transformations which preserve trace and positive semidefiniteness of operators,” Rep. Math. Phys
1972
Earlier work this paper cites.
R. S. Sutton, “Integrated architectures for learning, planning, and reacting based on approximating dynamic programming,” in Proceedings of the 7th International Conference on Machine Learning
1990
Earlier work this paper cites.
C. H. Bennett, G. Brassard, C. Crépeau, R. Jozsa, A. Peres, and W. K. Wootters, “Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels,” Phys. Rev. Lett
1993
Earlier work this paper cites.
C. H. Bennett, G. Brassard, S. Popescu, B. Schumacher, J. A. Smolin, and W. K. Wootters, “Purification of Noisy Entanglement and Faithful Teleportation via Noisy Channels,” Phys. Rev. Lett
1996
Earlier work this paper cites.
D. Deutsch, A. Ekert, R. Jozsa, C. Macchiavello, S. Popescu, and A. Sanpera, “Quantum Privacy Amplification and the Security of Quantum Cryptography over Noisy Channels,” Phys. Rev. Lett
1996
Earlier work this paper cites.
H.-J. Briegel, W. Dür, J. I. Cirac, and P. Zoller, “Quantum Repeaters: The Role of Imperfect Local Operations in Quantum Communication,” Phys. Rev. Lett
1998
Earlier work this paper cites.
D. Gottesman, “Theory of fault-tolerant quantum computation,” Phys. Rev. A
1998
Earlier work this paper cites.
P. O. Boykin, T. Mor, M. Pulver, V. Roychowdhury, and F. Vatan, “On universal and fault-tolerant quantum computing: A novel basis and a new constructive proof of universality for shor’s basis,” in Proceedings of the 40th Annual Symposium on Foundations of Computer Science
1999
Earlier work this paper cites.
P. W. Shor and J. Preskill, “Simple Proof of Security of the BB84 Quantum Key Distribution Protocol,” Phys. Rev. Lett
2000
Earlier work this paper cites.
X. Zhou, D. W. Leung, and I. L. Chuang, “Methodology for quantum logic gate construction,” Phys. Rev. A
2000
Earlier work this paper cites.
H.-K. Lo, “A simple proof of the unconditional security of quantum key distribution,” Journal of Physics A: Mathematical and General
2001
Earlier work this paper cites.
D. Gottesman and H.-K. Lo, “Proof of security of quantum key distribution with two-way classical communications,” IEEE Transactions on Information Theory
2003
Earlier work this paper cites.
J. Kempe, “Quantum random walks: An introductory overview,” Contemp. Phys
2003
Earlier work this paper cites.
R. Renner, “Security of Quantum Key Distribution,” PhD thesis, ETH Zurich
2005
Earlier work this paper cites.
A. Gilchrist, N. K. Langford, and M. A. Nielsen, “Distance measures to compare real and ideal quantum processes,” Phys. Rev. A
2005
Earlier work this paper cites.
W. Dür and H. J. Briegel, “Entanglement purification and quantum error correction,” Rep. Prog. Phys
2007
Earlier work this paper cites.
Kimble H. J., “The quantum internet,” Nature
2008
Earlier work this paper cites.
New Jersey: Prentice Hall, 3rd ed., 2010
S. Russel and P. Norvig, Artificial Intelligence - A Modern Approach · 2010
Earlier work this paper cites.
Adaptation, Learning, and Optimization, vol. 12, Berlin, Germany: Springer, 2012
M. Wiering and M. van Otterlo, eds., Reinforcement learning: State of the Art · 2012
Earlier work this paper cites.
H. J. Briegel and G. De las Cuevas, “Projective simulation for artificial intelligence,” Sci. Rep
2012
Earlier work this paper cites.
H. J. Briegel, “On creative machines and the physical origins of freedom,” Sci. Rep
2012
Earlier work this paper cites.
S. E. Venegas-Andraca, “Quantum walks: a comprehensive review,” Quantum Information Processing
2012
Earlier work this paper cites.
L. Orseau, T. Lattimore, and M. Hutter, “Universal knowledge-seeking agents for stochastic environments,” in Algorithmic Learning Theory
2013
Earlier work this paper cites.
Y.-B. Zhao and Z.-Q. Yin, “Apply current exponential de finetti theorem to realistic quantum key distribution,” International Journal of Modern Physics: Conference Series
2014
Earlier work this paper cites.
J. Bang, J. Ryu, S. Yoo, M. Pawłowski, and J. Lee, “A strategy for quantum algorithm design assisted by machine learning,” New J. Phys
2014
Earlier work this paper cites.
G. D. Paparo, V. Dunjko, A. Makmal, M. A. Martin-Delgado, and H. J. Briegel, “Quantum speed-up for active learning agents,” Phys. Rev. X
2014
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature
2015
Cited alongside, same era.
M. Tiersch, E. J. Ganahl, and H. J. Briegel, “Adaptive quantum computation in changing environments using projective simulation,” Sci. Rep
2015
Cited alongside, same era.
J. Mautner, A. Makmal, D. Manzano, M. Tiersch, and H. J. Briegel, “Projective simulation for classical learning agents: a comprehensive investigation,” New Gener. Comput
2015
Cited alongside, same era.
A. A. Melnikov, A. Makmal, and H. J. Briegel, “Benchmarking projective simulation in navigation problems,” IEEE Access
2018
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
2019
Closest in time.
L. O’Driscoll, R. Nichols, and P. A. Knott, “A hybrid machine learning algorithm for designing quantum experiments,” Quantum Mach. Intell
2019
Closest in time.
A. A. Melnikov, L. E. Fedichkin, and A. Alodjants, “Predicting quantum advantage by quantum walk with convolutional neural networks,” New J. Phys
2019
Closest in time.
H. Xu, J. Li, L. Liu, Y. Wang, H. Yuan, and X. Wang, “Generalizable control for quantum parameter estimation through reinforcement learning,” npj Quantum Information
2019
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V. Dunjko, N. Friis, and H. J. Briegel, “Quantum-enhanced deliberation of learning agents using trapped ions,” New J. Phys
2015
Cited alongside, same era.
N. Friis, A. A. Melnikov, G. Kirchmair, and H. J. Briegel, “Coherent controlization using superconducting qubits,” Sci. Rep
2015
Cited alongside, same era.
K. Azuma, K. Tamaki, and H.-K. Lo, “All-photonic quantum repeaters,” Nature Communications
2015
Cited alongside, same era.
D. Silver, A. Huang, C. 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
2016
Cited alongside, same era.
S. Hangl, E. Ugur, S. Szedmak, and J. Piater, “Robotic playing for hierarchical complex skill learning,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst
2016
Cited alongside, same era.
2016
Cited alongside, same era.
T. Mannucci and E.-J. van Kampen, “A hierarchical maze navigation algorithm with reinforcement learning and mapping,” in Proc. IEEE Symposium Series on Computational Intelligence
2016
Cited alongside, same era.
A. Makmal, A. A. Melnikov, V. Dunjko, and H. J. Briegel, “Meta-learning within projective simulation,” IEEE Access
2016
Cited alongside, same era.
Closest in time.
H. Poulsen Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel, and N. Friis, “Optimizing quantum error correction codes with reinforcement learning,” Quantum
2019
Closest in time.
A. Valenti, E. van Nieuwenburg, S. Huber, and E. Greplova, “Hamiltonian learning for quantum error correction,” Phys. Rev. Research
2019
Closest in time.
S. Yu, F. Albarrán-Arriagada, J. C. Retamal, Y.-T. Wang, W. Liu, Z.-J. Ke, Y. Meng, Z.-P. Li, J.-S. Tang, E. Solano, L. Lamata, C.-F. Li, and G.-C. Guo, “Reconstruction of a photonic qubit state with reinforcement learning,” Adv. Quantum Technol
2019
Closest in time.
J. Carrasquilla, G. Torlai, R. G. Melko, and L. Aolita, “Reconstructing quantum states with generative models,” Nat. Mach. Intell
2019
Closest in time.
G. Torlai, B. Timar, E. P. L. van Nieuwenburg, H. Levine, A. Omran, A. Keesling, H. Bernien, M. Greiner, V. Vuletić, M. D. Lukin, R. G. Melko, and M. Endres, “Integrating neural networks with a quantum simulator for state reconstruction,” Phys. Rev. Lett
2019
Closest in time.
A. Canabarro, S. Brito, and R. Chaves, “Machine learning nonlocal correlations,” Phys. Rev. Lett
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
S. Hangl, V. Dunjko, H. J. Briegel, and J. Piater, “Skill learning by autonomous robotic playing using active learning and exploratory behavior composition,” Front. Robot. AI
2020
Closest in time.
V. Dunjko and P. Wittek, “A non-review of Quantum Machine Learning: trends and explorations,” Quantum Views
2020
Closest in time.
M. Krenn, M. Erhard, and A. Zeilinger, “Computer-inspired quantum experiments,” arXiv:2002.09970
2020
Closest in time.
2020
Closest in time.
A. A. Melnikov, L. E. Fedichkin, R.-K. Lee, and A. Alodjants, “Machine learning transfer efficiencies for noisy quantum walks,” Adv. Quantum Technol
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
G. Liu, M. Chen, Y.-X. Liu, D. Layden, and P. Cappellaro, “Repetitive readout enhanced by machine learning,” Mach. Learn.: Sci. Technol
2020
Closest in time.
F. Flamini, A. Hamann, S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup, and H. J. Briegel, “Photonic architecture for reinforcement learning,” New J. Phys
2020
Closest in time.
Accessed: 2020-04-08
“Projective simulation Github repository.” github.com/qic-ibk/projectivesimulation · 2020
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