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Explicit demonstration of initial state construction in artificial neural networks using NetKet and IBM Q experience platform

  • Aditya Prasad Dash
  • Saket Kumar Sahu
  • Samikshya Kar
  • Bikash K. BeheraEmail author
  • Prasanta K. Panigrahi
Article
  • 83 Downloads

Abstract

Quantum neural networks have gained significant interest in recent times for the representation of many-body states and classical simulation of quantum computation. Here, we discuss the methods to generate specific initial states in the Restricted Boltzmann Machines analogous to those used as the initial states while performing quantum computation in quantum computers such as IBM Q. We validate our approach by applying the Pauli X gate to the single-qubit and two-qubit initial states using NetKet and compare the results to that obtained by performing the same task in the IBM quantum computer. We find that using this approach, the RBM neural networks can represent desired quantum states with high accuracy. Thus, this method is promising to mimic quantum computation classically in neural networks by using specific initial states.

Keywords

Quantum neural networks Restricted Boltzmann Machines Initial many-body states NetKet IBM quantum experience 

Notes

Acknowledgements

We gratefully acknowledge the discussions on using NetKet with Giuseppe Carleo, Developer Lead and Founder, NetKet. We also acknowledge the support of IBM quantum experience for producing experimental results. To the best of our knowledge, the explicit discussions on preparing initial states similar to that used in the quantum computers has not been published earlier, although significant amount of work has been done in this direction [5, 8, 10, 11]. A.P.D. is supported through KVPY fellowship and S.K. through Inspire fellowship, DST, Govt of India. A.P.D., S.K.S. and S.K. thank IISER Kolkata for providing hospitality during the course of the project. B.K.B. acknowledges the support of IISER-K Institute fellowship.

Supplementary material

References

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Aditya Prasad Dash
    • 1
  • Saket Kumar Sahu
    • 1
  • Samikshya Kar
    • 1
  • Bikash K. Behera
    • 2
    Email author
  • Prasanta K. Panigrahi
    • 2
  1. 1.Department of Physical SciencesIndian Institute of Science Education and Research BerhampurBerhampurIndia
  2. 2.Department of Physical SciencesIndian Institute of Science Education and Research KolkataMohanpurIndia

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