Fetching the paper…
Reading the bibliography…
Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks.
S. Kullback and R. A. Leibler, “On information and sufficiency,” Ann. Math. Statist. , vol. 22, no. 1, pp. 79–86, 1951
1951
Earlier work this paper cites.
I. Chakravarti, R. Laha, and J. Roy, Handbook of methods of applied statistics . Wiley, 1967
1967
Earlier work this paper cites.
F. Black and M. Scholes, “The pricing of options and corporate liabilities,” Journal of Political Economy , vol. 81, no. 3, pp. 637–654, 1973
1973
Earlier work this paper cites.
G. Thimm and E. Fiesler, “High-order and multilayer perceptron initialization,” IEEE Transactions on Neural Networks , vol. 8, no. 2, pp. 349–359, 1997
1997
Earlier work this paper cites.
A. Justel, D. Peña, and R. Zamar, “A multivariate kolmogorov-smirnov test of goodness of fit,” Statistics and Probability Letters , vol. 35, no. 3, pp. 251 – 259, 1997
1997
Earlier work this paper cites.
M. Lehtokangas and J. Saarinen, “Weight initialization with reference patterns,” Neurocomputing , vol. 20, no. 1, pp. 265 – 278, 1998
1998
Earlier work this paper cites.
L. K. Grover, “Synthesis of quantum superpositions by quantum computation,” Phys. Rev. Lett. , vol. 85, pp. 1334–1337, 2000
2000
Earlier work this paper cites.
J. Stanford, K. Giardina, G. Gerhardt, K. Fukumizu, and S.-i. Amari, “Local minima and plateaus in hierarchical structures of multilayer perceptrons,” Neural Networks , vol. 13, 2000
2000
Earlier work this paper cites.
G. Brassard, P. Hoyer, M. Mosca, and A. Tapp, “Quantum Amplitude Amplification and Estimation,” Contemporary Mathematics , vol. 305, 2002
2002
Earlier work this paper cites.
L. Grover and T. Rudolph, “Creating superpositions that correspond to efficiently integrable probability distributions,” 2002
2002
Earlier work this paper cites.
P. Glasserman, Monte Carlo Methods in Financial Engineering . Springer-Verlag New York, 2003
2003
Earlier work this paper cites.
F. Vatan and C. Williams, “Optimal quantum circuits for general two-qubit gates,” Physical Review A - Atomic, Molecular, and Optical Physics , vol. 69, no. 3, 2004
2004
Earlier work this paper cites.
V. V. Shende, S. S. Bullock, and I. L. Markov, “Synthesis of quantum logic circuits,” in Proceedings of the 2005 Asia and South Pacific Design Automation Conference , ser. ASP-DAC ’05. New York, NY, USA: ACM, 2005, pp. 272–275
2005
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, “Quantum algorithm for linear systems of equations,” Physical Review Letters , vol. 103, no. 15, 2009
2009
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information . Cambridge University Press, 2010
2010
Earlier work this paper cites.
M. Plesch and Č. Brukner, “Quantum-state preparation with universal gate decompositions,” Physical Review A , vol. 83, 2010
2010
Earlier work this paper cites.
M. Paris and J. Rehacek, “Quantum state estimation,” in Lecture Notes in Physics , 1st ed. Springer Publishing Company, Incorporated, 2010
2010
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nature Physics , vol. 10, 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 . Curran Associates, Inc., 2014, pp. 2672–2680
2014
Cited alongside, same era.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” 2014
2014
Cited alongside, same era.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations , 2014
2014
Cited alongside, same era.
S. Aaronson, “Read the fine print,” Nature Physics , vol. 11, pp. 291–293, 2015
2015
Cited alongside, same era.
Y. Burda, R. B. Grosse, and R. Salakhutdinov, “Importance weighted autoencoders,” 2015
2015
Cited alongside, same era.
D. Pedamonti, “Comparison of non-linear activation functions for deep neural networks on mnist classification task,” 2018
2018
Later among the works it cites.
P. Grnarova, K. Y. Levy, A. Lucchi, N. Perraudin, T. Hofmann, and A. Krause, “Evaluating gans via duality,” 2018
2018
Later among the works it cites.
P. Rebentrost, B. Gupt, and T. R. Bromley, “Quantum computational finance: Monte Carlo pricing of financial derivatives,” Physical Review A , vol. 98, no. 2, 2018
2018
Later among the works it cites.
J. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,” Nature Communications , vol. 9, 2018
2018
Later among the works it cites.
E. Farhi and H. Neven, “Classification with quantum neural networks on near term processors,” 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Mcclean, J. Romero, R. Babbush, and A. Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics , vol. 18, 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” IEEE International Conference on Computer Vision (ICCV 2015) , vol. 1502, 2015
2015
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016
2016
Cited alongside, same era.
V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville, “Adversarially learned inference,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein, “Unrolled generative adversarial networks,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
N. Kodali, J. D. Abernethy, J. Hays, and Z. Kira, “On convergence and stability of gans,” 2017
2017
Cited alongside, same era.
K. Roth, A. Lucchi, S. Nowozin, and T. Hofmann, “Stabilizing training of generative adversarial networks through regularization,” in NIPS , 2017
2017
Cited alongside, same era.
M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe, “Circuit-centric quantum classifiers,” 2018
2018
Later among the works it cites.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, “Quantum circuit learning,” Phys. Rev. A , vol. 98, p. 032309, 2018
2018
Later among the works it cites.
J.-G. Liu and L. Wang, “Differentiable learning of quantum circuit born machines,” Phys. Rev. A , vol. 98, p. 062324, 2018
2018
Later among the works it cites.
Y. Sanders, G. H. Low, A. Scherer, and D. W. Berry, “Black-box quantum state preparation without arithmetic,” Physical Review Letters , vol. 122, 2019
2019
Closest in time.
M. Benedetti, E. Grant, L. Wossnig, and S. Severini, “Adversarial quantum circuit learning for pure state approximation,” New Journal of Physics , vol. 21, 2019
2019
Closest in time.
L. Hu, S.-H. Wu, W. Cai, M. Yuwei, X. Mu, Y. Xu, H. Wang, Y. Song, D.-L. Deng, C.-L. Zou, and L. Sun, “Quantum generative adversarial learning in a superconducting quantum circuit,” Science Advances , vol. 5, 2019
2019
Closest in time.
J. Romero and A. Aspuru-Guzik, “Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions,” 2019
2019
Closest in time.
J. Zeng, Y. Wu, J.-G. Liu, L. Wang, and J. Hu, “Learning and inference on generative adversarial quantum circuits,” Phys. Rev. A , vol. 99, 2019
2019
Closest in time.
S. Woerner and D. J. Egger, “Quantum risk analysis,” npj Quantum Information , vol. 5, 2019
2019
Closest in time.
Y. Chen, Y. Chi, J. Fan, and C. Ma, “Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval,” Mathematical Programming , vol. 176, no. 1, pp. 5–37, 2019
2019
Closest in time.
H. Grimsley, S. Economou, E. Barnes, and N. Mayhall, “An adaptive variational algorithm for exact molecular simulations on a quantum computer,” Nature Communications , vol. 10, p. 3007, 2019
2019
Closest in time.
A. Harrow and J. Napp, “Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms,” 2019
2019
Closest in time.