Skip to main content
Have a personal or library account? Click to login
GuiTeNet: A Graphical User Interface for Tensor Networks Cover

GuiTeNet: A Graphical User Interface for Tensor Networks

Open Access
|Dec 2020

References

  1. Hackbusch, W and Kühn, S 2009 A new scheme for the tensor representation. J. Fourier Anal. Appl., 15: 706722. DOI: 10.1007/s00041-009-9094-9
  2. Hackbusch, W 2014 Numerical tensor calculus. Acta Numerica, 23: 651742. DOI: 10.1017/S0962492914000087
  3. Schollwöck, U 2011 The density-matrix renormalization group in the age of matrix product states. Ann. Phys., 326: 96192. DOI: 10.1016/j.aop.2010.09.012
  4. Verstraete, F, Murg, V and Cirac, J I 2008 Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems. Adv. Phys., 57: 143224. DOI: 10.1080/14789940801912366
  5. Vidal, G 2008 Class of quantum many-body states that can be efficiently simulated. Phys. Rev. Lett., 101: 110501. DOI: 10.1103/PhysRevLett.101.110501
  6. Pfeifer, R N C, Evenbly, G, Singh, S and Vidal, G 2014 NCON: A tensor network contractor for MATLAB. arXiv:1402.0939.
  7. Pfeifer, R N C, Haegeman, J and Verstraete, F 2014 Faster identification of optimal contraction sequences for tensor networks. Phys. Rev. E., 90: 033315. DOI: 10.1103/PhysRevE.90.033315
  8. Evenbly, G and Pfeifer, R N C 2014 Improving the efficiency of variational tensor network algorithms. Phys. Rev. B., 89: 245118. DOI: 10.1103/PhysRevB.89.245118
  9. Solomonik, E, Matthews, D, Hammond, J R, Stanton, J F and Demmel, J 2014 A massively parallel tensor contraction framework for coupled-cluster computations. Journal of Parallel and Distributed Computing, 74: 31763190. DOI: 10.1016/j.jpdc.2014.06.002
  10. Springer, P and Bientinesi, P 2016 Design of a high-performance GEMM-like tensortensor multiplication. arXiv:1607.00145.
  11. van der Walt, S, Colbert, S C and Varoquaux, G 2011 The NumPy array: A structure for effcient numerical computation. Comput. Sci. Eng., 13: 2230. DOI: 10.1109/MCSE.2011.37
  12. https://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html.
  13. Matthews, D 2018 High-performance tensor contraction without transposition. SIAM J. Sci. Comput., 40: C1C24. DOI: 10.1137/16M108968X
  14. Springer, P, Su, T and Bientinesi, P 2017 HPTT: A high-performance tensor transposition C++ library. In Proceedings of the 4th ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming, ARRAY 2017, pages 5662. ACM. DOI: 10.1145/3091966.3091968
  15. Ying, L 2017 Tensor network skeletonization. Multiscale Model. Sim., 15: 14231447. DOI: 10.1137/16M1082676
  16. Jouppi, N P, Young, C, Patil, N, et al. 2017 In-datacenter performance analysis of a Tensor Processing Unit. In ISCA’17 Proceedings of the 44th Annual International Symposium on Computer Architecture. DOI: 10.1145/3079856.3080246
  17. Evenbly, G 2019 TensorTrace: an application to contract tensor networks. arXiv:1911.02558.
DOI: https://doi.org/10.5334/jors.304 | Journal eISSN: 2049-9647
Language: English
Submitted on: Oct 1, 2019
Accepted on: Nov 18, 2020
Published on: Dec 15, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Lisa Sahlmann, Christian B. Mendl, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.