Quantum Machine Learning and Artificial Intelligence for Quantum Systems

About the Special Issue

The convergence of artificial intelligence and quantum information science creates new opportunities for computational innovation and quantum technology research. Quantum machine learning explores the potential of quantum computing for learning, inference, and data analysis, while artificial intelligence provides new tools for modelling, characterising, controlling, and optimising quantum systems.

Quantum Information & Computation (QIC) invites original research articles and high-quality reviews for this special issue. Contributions are welcome from researchers in computer science, mathematics, physics, engineering, and related application areas. All submissions must have a substantive connection to quantum machine learning, quantum information processing, or artificial intelligence methods for quantum systems and fall within the journal’s scope.

Topics of Interest

Topics include, but are not limited to:

• Theoretical foundations, algorithm design, and complexity analysis of quantum machine learning;
• Quantum neural networks, parameterised quantum circuits, and training methods;
• Hybrid quantum–classical learning frameworks and variational quantum algorithms;
• Data encoding, generalisation, trainability, and noise effects in quantum learning;
• AI-assisted quantum circuit design, compilation, and optimisation;
• Quantum state reconstruction, quantum process characterisation, and quantum system identification;
• AI applications in quantum control, device calibration, and experimental design;
• Machine learning for quantum error correction, noise modelling, and error mitigation;
• Interdisciplinary applications, performance evaluation, and reproducibility of quantum machine learning.

Application-focused submissions should clearly explain the quantum methods used, their research contributions, and the basis for evaluation. General AI applications without a substantive connection to quantum information or quantum systems fall outside the scope of this special issue.

Submission Requirements

Manuscripts must be written in English and follow the QIC author guidelines. Research articles should present clear research questions, sufficient methodological detail, and well-supported results. Review articles should provide systematic and critical analysis.

Submissions must be free of plagiarism and duplicate publication and must not be under consideration elsewhere. Authors must provide applicable statements on competing interests, funding, data and code availability, and research ethics in accordance with journal policies.

Editorial Management and Peer Review

The special issue will be overseen and managed by QIC’s existing Editorial Board. Editors with relevant expertise will conduct an initial assessment and arrange independent peer review for eligible submissions. Final editorial decisions will be made by the Editor-in-Chief or a formally authorised editor.

All submissions will follow the same academic standards, peer-review requirements, and publication ethics policies as regular submissions. Manuscripts involving an editorial conflict of interest will be handled independently by an editor without that conflict. Manuscript solicitation, author communications, and fee arrangements will not influence academic assessment. An invitation to submit does not guarantee acceptance.

Publication and Article Processing Charge

Accepted articles will be published open access and included in the special issue according to the journal’s publication schedule.

Article Processing Charge (APC): USD 2,950 per article, payable only after formal acceptance. Payment arrangements will be specified in the journal’s official notification and invoice.

Submission Details and Deadline

Submission deadline: 31 January 2027

Submission system: QIC Journal Manuscript Submission System (OJS)

Contact email: submissions@quantuminfocomputation.com

We welcome contributions that advance research at the intersection of artificial intelligence and quantum information science.

Quantum Information & Computation
Editorial Office