The deep learning and statistical physics applications to the problems of combinatorial optimization

Borin, A., & Abanin, D. A. (2019). Approximating power of machine-learning ansatz for quantum many-body states. ArXiv Preprint ArXiv:1901.08615

Original

1901

Earlier work this paper cites.

Yoshioka, N., & Hamazaki, R. (2019). Constructing neural stationary states for open quantum many-body systems. ArXiv Preprint ArXiv:1902.07006

Original

1902

Earlier work this paper cites.

Amizadeh, S., Matusevych, S., & Weimer, M. (2019). PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers. ArXiv Preprint ArXiv:1903.01969

Original

1903

Earlier work this paper cites.

Amizadeh, S., Matusevych, S., & Weimer, M. (2019). PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers. ArXiv Preprint ArXiv:1903.01969

Original

1903

Earlier work this paper cites.

Choo, K., Neupert, T., & Carleo, G. (2019). Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States. ArXiv Preprint ArXiv:1903.06713

Original

1903

Earlier work this paper cites.

McBrian, K., Carleo, G., & Khatami, E. (2019). Ground state phase diagram of the one-dimensional Bose-Hubbard model from restricted Boltzmann machines. ArXiv Preprint ArXiv:1903.03076

Original

1903

Earlier work this paper cites.

Carleo, Giuseppe, et al. (2019). NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems. ArXiv Preprint ArXiv:1904.00031

Original

1904

Earlier work this paper cites.

Gasse, M., Chételat, D., Ferroni, N., Charlin, L., & Lodi, A. (2019). Exact Combinatorial Optimization with Graph Convolutional Neural Networks. ArXiv Preprint ArXiv:1906.01629

Original

1906

Earlier work this paper cites.

Glen Bigan Mbeng, Rosario Fazio, Giuseppe Santoro (2019). Quantum Annealing: a journey through Digitalization, Control, and hybrid Quantum Variational schemes. ArXiv Preprint ArXiv:1906.08948

Original

1906

Earlier work this paper cites.

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21(6), 1087–1092

1953

Earlier work this paper cites.

Vapnik, V., & Chervonenkis, A. J. (1974). Theory of Pattern Recognition

1974

Earlier work this paper cites.

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680

1983

Cited alongside, same era.

Barahona, F., Grötschel, M., Jünger, M., & Reinelt, G. (1988). An application of combinatorial optimization to statistical physics and circuit layout design. Operations Research, 36(3), 493–513

1988

Cited alongside, same era.

Greig, D. M., Porteous, B. T., & Seheult, A. H. (1989). Exact Maximum A Posteriori Estimation for Binary Images. Journal of the Royal Statistical Society Series b-Methodological, 51(2), 271–279

1989

Cited alongside, same era.

Cybenko, George. ”Approximations by superpositions of a sigmoidal function.” Mathematics of Control, Signals and Systems 2 (1989): 183-192

1989

Cited alongside, same era.

White, S. R. (1992). Density matrix formulation for quantum renormalization groups. Physical Review Letters, 69(19), 2863–2866

Wiegele, A. (2007). Biq Mac Library—A collection of Max-Cut and quadratic 0-1 programming instances of medium size. Preprint

2007

Later among the works it cites.

Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008). Exploring Network Structure, Dynamics, and Function using NetworkX

2008

Later among the works it cites.

Bian, Y., Gronskiy, A., & Buhmann, J. M. (2016). Information-theoretic analysis of MaxCut algorithms. In 2016 Information Theory and Applications Workshop (ITA) (pp. 1–5)

2016

Later among the works it cites.

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … Isard, M. (2016). TensorFlow: a system for large-scale machine learning. Operating Systems Design and Implementation, 265–283

2016

Later among the works it cites.

Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations