Advances in the Design and Optimization of Quantum Circuits with Classical Machine Learning
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 66
Special Issue Editor
Special Issue Information
Dear Colleagues,
Designing and optimizing quantum circuits that both solve target problems and respect device limitations is central to achieving practical quantum advantage. This is crucial in the noisy intermediate-scale and emerging fault-tolerant eras. Classical machine learning has emerged as a powerful tool in this context, providing flexible techniques to automatically synthesize, compress, and tune quantum circuits under realistic hardware constraints. This Special Issue, ‘Advances in the Design and Optimization of Quantum Circuits with Classical Machine Learning’, aims to bring together recent theoretical, algorithmic, and applied developments in this rapidly evolving field. We invite original research and review articles on topics including, but not limited to, the following:
- Learning-based quantum circuit synthesis and compilation;
- Hardware-aware and hardware-agnostic quantum architecture search;
- Reinforcement learning for circuit optimization;
- Noise-aware training and error-mitigation strategies;
- Machine learning-assisted simulation or verification of quantum circuits.
Applications spanning quantum chemistry, optimization, quantum communications, and quantum-enhanced machine learning are particularly welcome. Contributions that provide open benchmarks, software frameworks, or reproducible workflows are strongly encouraged, as they will help shape robust standards and accelerate progress in this emerging research area.
Dr. Akash Kundu
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- quantum circuit design
- quantum circuit optimization
- quantum architecture search
- machine learning
- reinforcement learning
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