Quantum Machine Intelligence publishes original articles on cutting-edge experimental and theoretical research in all areas of quantum artificial intelligence. The Journal is unique in promoting a synthesis of machine learning, data science and computational intelligence research with quantum computing developments. Its primary goal is to foster the utilization of quantum computing for real-world problems so as to pave the way towards the next generation of artificial intelligence systems. The Journal also publishes innovative papers reporting on machine intelligence theories, methods and applications inspired by physics and nature, e.g. computational intelligence, fuzzy systems, evolutionary computation, machine and deep learning.

All papers submitted undergo a rigorous peer review to ensure their originality, timeliness, relevance and readability. The journal also welcomes occasional review articles and short communications in all of the above-mentioned topic areas.

  • The first scientific journal that highlights the synergies between quantum computing and artificial intelligence
  • Promotes a synthesis of the research areas of machine learning and data science, and their engineering applications based on quantum technologies
  • Fosters the application of quantum computing and artificial intelligence to address real-world problems

Journal information

Editor-in-Chief
  • Giovanni Acampora
Publishing model
Hybrid. Open Access options available

Journal metrics

113 days
Submission to first decision
148 days
Submission to acceptance
11,127 (2019)
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Latest articles

  1. Authors (first, second and last of 4)

    • Balthazar Casalé
    • Giuseppe Di Molfetta
    • Liva Ralaivola
    • Content type: Research Article
This journal has 5 open access articles

About this journal

Electronic ISSN
2524-4914
Print ISSN
2524-4906
Abstracted and indexed in
  1. ACM Digital Library
  2. CNKI
  3. Dimensions
  4. EBSCO Discovery Service
  5. EI Compendex
  6. Google Scholar
  7. INSPEC
  8. Institute of Scientific and Technical Information of China
  9. Japanese Science and Technology Agency (JST)
  10. Naver
  11. OCLC WorldCat Discovery Service
  12. ProQuest-ExLibris Primo
  13. ProQuest-ExLibris Summon
  14. WTI Frankfurt eG
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