Control Theory and Technology

, Volume 17, Issue 4, pp 393–395 | Cite as

New directions in quantum neural networks research

  • Wei CuiEmail author
  • Shilu Yan


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  1. [1]
    I. Cong, S. Choi, M. D. Lukin. Quantum convolutional neural networks. Nature Physics, 2019: DOI Scholar
  2. [2]
    M. Schuld, I. Sinayskiy, F. Petruccione. The quest for a quantum neural network. Quantum Information Processing, 2014, 13(11): 2567–2586.MathSciNetCrossRefGoogle Scholar
  3. [3]
    G. Vidal. Class of quantum many-body states that can be efficiently simulated. Physical Review Letters, 2008, 101(11): DOI
  4. [4]
    G. Carleo, M. Troyer. Solving the quantum many-body problem with artificial neural networks. Science, 2017, 355(6325): 602–606.MathSciNetCrossRefGoogle Scholar
  5. [5]
    Z. Cai, J. Liu. Approximating quantum many-body wave functions using artificial neural networks. Physical Review B, 2018, 976(3): DOI
  6. [6]
    A. Nagy, V. Savona. Variational quantum Monte Carlo method with a neural-network ansatz for open quantum systems. Physical Review Letters, 2019, 122(25): DOI
  7. [7]
    M. J. Hartmann, G. Carleo. Neural-network approach to dissipative quantum many-body dynamics. Physical Review Letters, 2019, 122(25): DOI
  8. [8]
    F. Vicentini, A. Biella, N. Regnault, et al. Variational neural-network ansatz for steady states in open quantum systems. Physical Review Letters, 2019, 122(25): DOI
  9. [9]
    D. Pfau, J. S. Spencer, A. G. G. Matthews, et al. Ab-Initio solution of the many-electron Schrodinger equation with deep neural networks. arXiv, 2019: arXiv:1909.02487.Google Scholar
  10. [10]
    J. Carrasquilla, R. G. Melko. Machine learning phases of matter. Nature Physics, 2017, 13(5): 431–434.CrossRefGoogle Scholar
  11. [11]
    P. Zhang, H. Shen, H. Zhai. Machine learning topological invariants with neural networks. Physical Review Letters, 2018, 120(6): DOI
  12. [12]
    J. Gao, L. Qiao, Z. Jiao, et al. Experimental machine learning of quantum states. Physical Review Letters, 2018, 120(24): DOI
  13. [13]
    K. T. Butler, D. W. Davies, H. Cartwright, et al. Machine learning for molecular and materials science. Nature, 2018, 559(7715): 547–555.CrossRefGoogle Scholar
  14. [14]
    C. Chen, W. Ye, Y. Zuo, et al. Graph networks as a universal machine learning framework for molecules and crystals. Chemistry of Materials, 2019, 31(9): 3564–3572.CrossRefGoogle Scholar
  15. [15]
    A. Daskin. A simple quantum neural net with a periodic activation function. IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, Japan: IEEE, 2018: 2887–2891.Google Scholar
  16. [16]
    P. Rebentrost, T. R. Bromley, C. Weedbrook, et al. Quantum hopfield neural network. Physical Review A, 2018, 98(4): DOI
  17. [17]
    J. R. McClean, S. Boixo, V. N. Smelyanskiy, et al. Barren plateaus in quantum neural network training landscapes. Nature Communications, 2018, 9: DOI
  18. [18]
    S. Lloyd, C. Weedbrook. Quantum generative adversarial learning. Physical Review Letters, 2018, 121(4): DOI
  19. [19]
    P. L. Dallaire-Demers, N. Killoran. Quantum generative adversarial networks. Physical Review A, 2018, 986(1): DOI

Copyright information

© South China University of Technology, Academy of Mathematics and Systems Science, CAS and Springer Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Automation Science and EngineeringSouth China University of TechnologyGuangzhou GuangdongChina

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