Machine Learning in Classical and Quantum Photonic Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".
Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 508
Special Issue Editors
Interests: smart quantum imaging; quantum optics; quantum information; open quantum systems; quantum-enhanced nonlinear spectroscopy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The last decade has witnessed a considerable burst of experimental and theoretical work combining two seemingly different fields, namely artificial intelligence and photonics. These investigations have shown the potential of machine learning for designing, controlling and interacting with complex photonic systems. More importantly, they have arguably placed machine learning as a new route of innovation, capable of transforming future quantum (and classical) photonic technologies.
Machine-learning-assisted photonics is a timely and exciting research field at the forefront of physics and technology, with much potential to impact many areas of science and engineering, including material science, communications, sensing, imaging and spectroscopy. Motivated by the increasing number of members in this fascinating community, we are happy to announce a Special Issue of Entropy focused on new developments that lie at the interface of machine learning and quantum (and classical) photonics. Through this Special Issue, we intend to advance our understanding of machine-learning-enabled photonics, aiming at developing new technologies that could have a positive impact on our way of life.
This Special Issue will be focused on theoretical and experimental contributions that bring together the fields of quantum and classical optical technologies and machine learning. Topics covered include, but are not limited to:
- Machine learning for optical applications (including advances in quantum metrology, imaging, microscopy, optical coherence tomography, etc.);
- Implementation of intelligent systems using photonic technologies;
- Machine-learning-enabled inverse design of quantum photonic devices;
- Classification of non-classical quantum states using machine learning;
- Machine-learning-assisted holography and data encryption;
- Neural network quantum-state tomography;
- Machine learning for quantum communication.
Dr. Roberto de J. León-Montiel
Dr. Jiří Svozilík
Guest Editors
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