SCIRP Mobile Website

Why Us? >>

  • - Open Access
  • - Peer-reviewed
  • - Rapid publication
  • - Lifetime hosting
  • - Free indexing service
  • - Free promotion service
  • - More citations
  • - Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

 

Contact Us >>

WhatsApp  +86 18163351462(WhatsApp)
   
Paper Publishing WeChat
Book Publishing WeChat
(or Email:book@scirp.org)

Article citations

More>>

Dong, D.Y., Chen, C.L., Li, H.X. and Tarn, T.-J. (2008) Quantum Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38, 1207-1220.
https://doi.org/10.1109/TSMCB.2008.925743

has been cited by the following article:

  • TITLE: Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

    AUTHORS: Wei Hu, James Hu

    KEYWORDS: Continuous-Variable Quantum Computers, Quantum Machine Learning, Quantum Reinforcement Learning, Contextual Multi-Armed Bandit Problem

    JOURNAL NAME: Natural Science, Vol.11 No.1, January 18, 2019

    ABSTRACT: Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.