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A Probabilistic Evolutionary Optimization Approach to Compute Quasiparticle Braids

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

This paper proposes the use of estimation of distribution algorithms to deal with the problem of finding an optimal product of braid generators in topological quantum computing. We investigate how the regularities of the braid optimization problem can be translated into statistical regularities by means of the Boltzmann distribution. The introduced algorithm obtains solutions with an accuracy in the order of 10− 6, and lengths up to 9 times shorter than those expected from braids of the same accuracy obtained with other methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV/EHU), P. Manuel de Lardizabal, 20018, Guipuzcoa, Spain

    Roberto Santana

  2. Department of Physics and Astronomy, Texas A&M University, College Station, Texas, 77843-4242, USA

    Ross B. McDonald & Helmut G. Katzgraber

  3. Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA

    Helmut G. Katzgraber

Authors
  1. Roberto Santana
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  2. Ross B. McDonald
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  3. Helmut G. Katzgraber
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Santana, R., McDonald, R.B., Katzgraber, H.G. (2014). A Probabilistic Evolutionary Optimization Approach to Compute Quasiparticle Braids. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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