Fetching the paper…
Reading the bibliography…
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.
E. Ising, “Contribution to the theory of ferromagnetism,” Z. Phys
1925
Earlier work this paper cites.
Rand Corporation research study, Princeton University Press, 1957
R. Bellman, R. Corporation, and K. M. R. Collection, Dynamic Programming · 1957
Earlier work this paper cites.
W. K. Hastings, “Monte carlo sampling methods using markov chains and their applications,” 1970
1970
Earlier work this paper cites.
V. Vapnik and A. Y. Chervonenkis, “On the uniform convergence of relative frequencies of events to their probabilities,” Measures of Complexity
1971
Earlier work this paper cites.
D. J. Aldous, “Some inequalities for reversible markov chains,” Journal of the London Mathematical Society
1982
Earlier work this paper cites.
G. E. Hinton and T. J. Sejnowski, “Optimal perceptual inference,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition
1983
Earlier work this paper cites.
S. Geman and D. Geman, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” IEEE Transactions on pattern analysis and machine intelligence
1984
Earlier work this paper cites.
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, “A learning algorithm for boltzmann machines,” Cognitive science
1985
Earlier work this paper cites.
M. R. Jerrum, L. G. Valiant, and V. V. Vazirani, “Random generation of combinatorial structures from a uniform distribution,” Theoretical computer science
1986
Earlier work this paper cites.
C. Durr and P. Hoyer, “A quantum algorithm for finding the minimum,” arXiv preprint quant-ph/9607014
1996
Earlier work this paper cites.
J. N. Tsitsiklis and B. Van Roy, “Analysis of temporal-diffference learning with function approximation,” in Advances in neural information processing systems
1997
Earlier work this paper cites.
M. Welling and G. E. Hinton, “A new learning algorithm for mean field boltzmann machines,” in International Conference on Artificial Neural Networks
2002
Earlier work this paper cites.
G. Brassard, M. Mosca, and A. Tapp, “Quantum amplitude amplification and estimation,” Contemporary Mathematics
2002
Earlier work this paper cites.
2002
Earlier work this paper cites.
B. Sallans and G. E. Hinton, “Reinforcement learning with factored states and actions,” Journal of Machine Learning Research
2004
Earlier work this paper cites.
M. Szegedy, “Quantum speed-up of markov chain based algorithms,” in Foundations of Computer Science, 2004. Proceedings. 45th Annual IEEE Symposium on
2004
Earlier work this paper cites.
M. A. Carreira-Perpinan and G. E. Hinton, “On contrastive divergence learning.,” in Aistats
2005
Earlier work this paper cites.
Y. Lecun, S. Chopra, R. Hadsell, M. A. Ranzato, and F. J. Huang, “A tutorial on energy-based learning,” in Predicting structured data
2006
Earlier work this paper cites.
G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural computation
2006
Earlier work this paper cites.
Springer Science & Business Media, 2006
J. Nocedal and S. Wright, Numerical optimization · 2006
Earlier work this paper cites.
2008
Earlier work this paper cites.
V. Giovannetti, S. Lloyd, and L. Maccone, “Quantum random access memory,” Physical review letters
2008
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.
2008
Earlier work this paper cites.
P. Wocjan and A. Abeyesinghe, “Speedup via quantum sampling,” Physical Review A
2008
Earlier work this paper cites.
R. D. Somma, S. Boixo, H. Barnum, and E. Knill, “Quantum simulations of classical annealing processes,” Physical review letters
2008
Earlier work this paper cites.
R. Salakhutdinov and G. Hinton, “Deep boltzmann machines,” in Artificial in telligence and statistics
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
M. Otsuka, J. Yoshimoto, and K. Doya, “Free-energy-based reinforcement learning in a partially observable environment.,” in ESANN
2010
Earlier work this paper cites.
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 (ICML-10)
2010
Earlier work this paper cites.
Cambridge University Press, 2010
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information · 2010
Earlier work this paper cites.
J. Ngiam, Z. Chen, P. W. Koh, and A. Y. Ng, “Learning deep energy models,” in Proceedings of the 28th international conference on machine learning (ICML-11)
2011
Earlier work this paper cites.
A. Fischer and C. Igel, “Bounding the bias of contrastive divergence learning,” Neural computation
2011
Earlier work this paper cites.
G. F. Montúfar, J. Rauh, and N. Ay, “Expressive power and approximation errors of restricted boltzmann machines,” in Advances in neural information processing systems
2011
Earlier work this paper cites.
F. Magniez, A. Nayak, J. Roland, and M. Santha, “Search via quantum walk,” SIAM Journal on Computing
2011
Earlier work this paper cites.
K. Temme, T. J. Osborne, K. G. Vollbrecht, D. Poulin, and F. Verstraete, “Quantum metropolis sampling,” Nature
2011
Earlier work this paper cites.
N. Wiebe, D. Braun, and S. Lloyd, “Quantum algorithm for data fitting,” Physical review letters
2012
Earlier work this paper cites.
H. J. Briegel and G. De las Cuevas, “Projective simulation for artificial intelligence,” Scientific reports
2012
Earlier work this paper cites.
G. E. Hinton, “A practical guide to training restricted boltzmann machines,” in Neural networks: Tricks of the trade
2012
Earlier work this paper cites.
M.-H. Yung and A. Aspuru-Guzik, “A quantum–quantum metropolis algorithm,” Proceedings of the National Academy of Sciences
2012
Cited alongside, same era.
N. Srivastava and R. R. Salakhutdinov, “Multimodal learning with deep boltzmann machines,” in Advances in neural information processing systems
2012
Cited alongside, same era.
A. Fischer and C. Igel, “An introduction to restricted boltzmann machines,” in Iberoamerican Congress on Pattern Recognition
2012
Cited alongside, same era.
N. Heess, D. Silver, and Y. W. Teh, “Actor-critic reinforcement learning with energy-based policies,” in Proceedings of the Tenth European Workshop on Reinforcement Learning
2013
Cited alongside, same era.
J. Martens, A. Chattopadhya, T. Pitassi, and R. Zemel, “On the representational efficiency of restricted boltzmann machines,” in Advances in Neural Information Processing Systems
A. Cornelissen, “Quantum gradient estimation and its application to quantum reinforcement learning,” 2018
2018
Later among the works it cites.
M. August and J. M. Hernández-Lobato, “Taking gradients through experiments: Lstms and memory proximal policy optimization for black-box quantum control,” in International Conference on High Performance Computing
2018
Later among the works it cites.
T. Fösel, P. Tighineanu, T. Weiss, and F. Marquardt, “Reinforcement learning with neural networks for quantum feedback,” Physical Review X
2018
Later among the works it cites.
M. Bukov, A. G. Day, D. Sels, P. Weinberg, A. Polkovnikov, and P. Mehta, “Reinforcement learning in different phases of quantum control,” Physical Review X
2018
Later among the works it cites.
M. Bukov, “Reinforcement learning for autonomous preparation of floquet-engineered states: Inverting the quantum kapitza oscillator,” Physical Review B
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Physical review letters
2014
Cited alongside, same era.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nature Physics
2014
Cited alongside, same era.
G. D. Paparo, V. Dunjko, A. Makmal, M. A. Martin-Delgado, and H. J. Briegel, “Quantum speedup for active learning agents,” Physical Review X
2014
Cited alongside, same era.
T. J. Yoder, G. H. Low, and I. L. Chuang, “Fixed-point quantum search with an optimal number of queries,” Physical review letters
2014
Cited alongside, same era.
M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemporary Physics
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2018
Later among the works it cites.
A. A. Melnikov, H. P. Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proceedings of the National Academy of Sciences
2018
Later among the works it cites.
2018
Later among the works it cites.
S. S. Vedaie, P. Palittapongarnpim, and B. C. Sanders, “Reinforcement learning for quantum metrology via quantum control,” in 2018 IEEE Photonics Society Summer Topical Meeting Series (SUM)
2018
Later among the works it cites.
M. Hessel, J. Modayil, H. Van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver, “Rainbow: Combining improvements in deep reinforcement learning,” in Thirty-Second AAAI Conference on Artificial Intelligence
2018
Later among the works it cites.
D. Crawford, A. Levit, N. Ghadermarzy, J. S. Oberoi, and P. Ronagh, “Reinforcement learning using quantum boltzmann machines,” Quantum Information & Computation
2018
Later among the works it cites.
A. A. Melnikov, A. Makmal, and H. J. Briegel, “Benchmarking projective simulation in navigation problems,” IEEE Access
2018
Later among the works it cites.
D. Orsucci, H. J. Briegel, and V. Dunjko, “Faster quantum mixing for slowly evolving sequences of markov chains,” Quantum
2018
Later among the works it cites.
2018
Later among the works it cites.
X. Gao, Z.-Y. Zhang, and L.-M. Duan, “A quantum machine learning algorithm based on generative models,” Science advances
2018
Later among the works it cites.
2019
Closest in time.
2019
Closest in time.
H. P. Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel, and N. Friis, “Optimizing quantum error correction codes with reinforcement learning,” Quantum
2019
Closest in time.
R. Porotti, D. Tamascelli, M. Restelli, and E. Prati, “Coherent transport of quantum states by deep reinforcement learning,” Communications Physics
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
J. Wu and T. H. Hsieh, “Variational thermal quantum simulation via thermofield double states,” Physical Review Letters
2019
Closest in time.
2019
Closest in time.
G. H. Low and I. L. Chuang, “Hamiltonian simulation by qubitization,” Quantum
2019
Closest in time.
Y. Subaşı, L. Cincio, and P. J. Coles, “Entanglement spectroscopy with a depth-two quantum circuit,” Journal of Physics A: Mathematical and Theoretical
2019
Closest in time.
2019
Closest in time.
V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature
2019
Closest in time.
M. Schuld and N. Killoran, “Quantum machine learning in feature hilbert spaces,” Physical review letters
2019
Closest in time.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” Physical Review A
2020
Closest in time.
H. J. Kappen, “Learning quantum models from quantum or classical data,” Journal of Physics A: Mathematical and Theoretical
2020
Closest in time.
M. Dalgaard, F. Motzoi, J. J. Sorensen, and J. Sherson, “Global optimization of quantum dynamics with alphazero deep exploration,” npj Quantum Information
2020
Closest in time.
A. W. Harrow and A. Y. Wei, “Adaptive quantum simulated annealing for bayesian inference and estimating partition functions,” in Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms
2020
Closest in time.
J. Van Apeldoorn, A. Gilyén, S. Gribling, and R. de Wolf, “Quantum sdp-solvers: Better upper and lower bounds,” Quantum
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.