Routing in quantum communication networks using reinforcement machine learning

Article Properties
Abstract
Cite
Roik, Jan, et al. “Routing in Quantum Communication Networks Using Reinforcement Machine Learning”. Quantum Information Processing, vol. 23, no. 3, 2024, https://doi.org/10.1007/s11128-024-04287-z.
Roik, J., Bartkiewicz, K., Černoch, A., & Lemr, K. (2024). Routing in quantum communication networks using reinforcement machine learning. Quantum Information Processing, 23(3). https://doi.org/10.1007/s11128-024-04287-z
Roik, Jan, Karol Bartkiewicz, Antonín Černoch, and Karel Lemr. “Routing in Quantum Communication Networks Using Reinforcement Machine Learning”. Quantum Information Processing 23, no. 3 (2024). https://doi.org/10.1007/s11128-024-04287-z.
Roik J, Bartkiewicz K, Černoch A, Lemr K. Routing in quantum communication networks using reinforcement machine learning. Quantum Information Processing. 2024;23(3).
Journal Categories
Science
Mathematics
Science
Physics
Refrences
Title Journal Journal Categories Citations Publication Date
Advances in the quantum internet

Communications of the ACM
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
31 2022
10.1109/COMST.2022.3144219 2022
10.1109/COMST.2021.3109944 2021
Potential key technologies for 6G mobile communications Science China Information Sciences
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
75 2020
10.1109/MNET.001.1900092 IEEE Network
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Technology: Technology (General): Industrial engineering. Management engineering: Information technology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
2019
Refrences Analysis
The category Science: Physics 21 is the most frequently represented among the references in this article. It primarily includes studies from Physical Review A and Physical Review Letters. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year