IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/3511029.html
   My bibliography  Save this article

Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

Author

Listed:
  • Tomoaki Kimura
  • Kodai Shiba
  • Chih-Chieh Chen
  • Masaru Sogabe
  • Katsuyoshi Sakamoto
  • Tomah Sogabe

Abstract

Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.

Suggested Citation

  • Tomoaki Kimura & Kodai Shiba & Chih-Chieh Chen & Masaru Sogabe & Katsuyoshi Sakamoto & Tomah Sogabe, 2021. "Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, December.
    • Handle: RePEc:hin:jnlmpe:3511029
    DOI: 10.1155/2021/3511029
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/3511029.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/3511029.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/3511029?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:3511029. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.