Recent Advances in Quantum Machine Learning
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 423
Editors
Interests: quantum machine learning; quantum computation; quantum program quality assurance
Special Issue Information
Dear Colleagues,
The intersection of quantum computing and machine learning has emerged as one of the most promising frontiers in modern computational science. As classical machine learning models grow increasingly complex and data-intensive, quantum computing offers fundamentally new computational paradigms that may overcome key limitations in speed, expressibility, and energy efficiency. Quantum machine learning (QML) seeks to harness quantum mechanical phenomena, such as superposition, entanglement, and quantum interference, to accelerate and enhance learning algorithms.
Recent advances in variational quantum circuits, quantum neural networks, and quantum gradient descent methods have demonstrated encouraging results in tasks such as classification, optimization, and function approximation. Hybrid quantum–classical architectures, which combine the strengths of both paradigms, have attracted particular attention as near-term quantum hardware continues to mature. Meanwhile, information-theoretic perspectives, including quantum entropy measures and channel capacity, provide rigorous frameworks for understanding generalization, expressibility, and the fundamental limits of quantum learning systems.
This Special Issue invites contributions that explore the full spectrum of recent advances in quantum machine learning, including, but not limited to, novel quantum learning algorithms, hybrid quantum–classical models, quantum optimization methods, theoretical analyses using information and entropy measures, and practical implementations on near-term quantum devices. We welcome both theoretical and applied work and encourage submissions that bridge quantum information theory with machine learning practice.
Dr. Nan Jiang
Dr. Jian Wang
Guest Editors
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy 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 2600 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 machine learning
- variational quantum circuits
- quantum neural networks
- hybrid quantum–classical models
- quantum optimization
- quantum gradient descent
- quantum information theory
- quantum entropy
- near-term quantum devices
- quantum computing
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