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Approximately Learning Quantum Automata

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Theoretical Aspects of Software Engineering (TASE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13931))

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Abstract

In this paper, we provide two methods for learning measure-once one-way quantum finite automata using a combination of active learning and non-linear optimization. First, we learn the number of states of a measure-once one-way quantum automaton using a heuristic binary tree representing the different variations of a Hankel matrix. Then we use two optimization methods to learn the unitary matrices representing the transitions of the automaton. When comparing the original automaton with the one learned, we provide a new way to compute the distance on the base of the language of the combined quantum automata. Finally, we show, using experiments on a set of randomly generated quantum automata, which method performs better.

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Authors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Wenjing Chu & Marcello Bonsangue

  2. University of Amsterdam, Amsterdam, The Netherlands

    Shuo Chen

  3. I2R, Agency for Science, Technology and Research, Singapore, Singapore

    Zenglin Shi

Authors
  1. Wenjing Chu
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  2. Shuo Chen
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  3. Marcello Bonsangue
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  4. Zenglin Shi
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Corresponding authors

Correspondence to Wenjing Chu or Marcello Bonsangue .

Editor information

Editors and Affiliations

  1. University of Bristol, Bristol, UK

    Cristina David

  2. Peking University, Beijing, China

    Meng Sun

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Chu, W., Chen, S., Bonsangue, M., Shi, Z. (2023). Approximately Learning Quantum Automata. In: David, C., Sun, M. (eds) Theoretical Aspects of Software Engineering. TASE 2023. Lecture Notes in Computer Science, vol 13931. Springer, Cham. https://doi.org/10.1007/978-3-031-35257-7_16

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  • DOI: https://doi.org/10.1007/978-3-031-35257-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35256-0

  • Online ISBN: 978-3-031-35257-7

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