Quantum Machine Intelligence

Aims & Scope

This single-blind peer-reviewed journal 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.

Selected areas and topics of interest include, but are not limited to: 1) Quantum Machine Learning 2) Quantum Computing for Artificial Intelligence 3) Artificial Intelligence for Quantum Information Processing 4) Quantum and Bio-inspired Computational Intelligence 5) Quantum Annealing and Optimization

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	4.4
2024	4.10

SJR (SCImago Journal Rank)

Year	Value
2024	1.059

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	23

Journal Rank

Year	Value
2024	4410

Journal Citation Indicator

Year	Value
2024	628

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Quanvolutional neural networks: powering image recognition with quantum circuits

  Citation: 322

  Authors: Maxwell, Samriddhi, Shashindra, Tristan

• Quantum convolutional neural network for classical data classification

  Citation: 234

  Authors: Tak, Leeseok, Daniel K.

• Layerwise learning for quantum neural networks

  Citation: 220

  Authors: Andrea, Jarrod R., Masoud, Patrick, Martin

• Reverse quantum annealing approach to portfolio optimization problems

  Citation: 167

  Authors: Davide, Alexei

• Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability

  Citation: 93

  Authors: Thomas, Josef, Patrick, Koen

• Variational quantum Boltzmann machines

  Citation: 76

  Authors: Christa, Aurélien, Stefan

• Quantum transfer learning for breast cancer detection

  Citation: 71

  Authors: Vanda, Carla, Inês

• An introduction to quantum machine learning: from quantum logic to quantum deep learning

  Citation: 60

  Authors: Leonardo, Davide, Pietro, Simone

• Universal discriminative quantum neural networks

  Citation: 56

  Authors: H., L., S., H., M.

• Financial fraud detection using quantum graph neural networks

  Citation: 55

  Authors: Nouhaila, Abhishek, Ashim, Siddhant, Sairupa, Husayn, Nandan, Muhammad Al-Zafar, Ioannis, Mohamed

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