Machine Learning and Artificial Intelligence in Quantum Computing Platforms
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Quantum Electronics".
Deadline for manuscript submissions: closed (1 August 2022) | Viewed by 2310
Special Issue Editors
Interests: machine learning-based measurement and control of quantum experiments; explainable AI; uncertainty quantification in machine learning; science education
Interests: radio-frequency reflectometry for fast and sensitive readout of spin qubits and carbon nanotube electromechanics; realizing thermodynamics experiments; machine learning for qubit scalability
Interests: research at the interface of quantum technologies, artificial intelligence, and condensed matter physics; automation of quantum devices; reconstruction of the key parameters for quantum system dynamics
Special Issue Information
Dear Colleagues,
As the complexity of quantum devices increases, groundbreaking experimental work is evidencing the potential of machine learning approaches for the development and automation of new quantum technologies. Among the forefront challenges in scaling up contemporary quantum computing platforms are reliable fabrication, large arrays design, and the time-consuming procedures necessary to achieve the high-level control required to operate quantum devices. In response to these challenges, a new field has begun to form at the boundary of quantum devices and artificial intelligence, where the versatility and generalization ability of the latter is being used to achieve optimal quantum control.
This Special Issue targets this emerging field, focusing on advances in machine-learning-enhanced control, calibration, and fabrication of quantum devices in a range of quantum computing platforms. Of special interest is the application of machine learning methods to experiments, focusing on the control of quantum circuits as well as machine learning software for quantum devices. We welcome submissions ranging from analysis of data sets produced by experiments, to experimental design solutions using artificial intelligence, reinforcement learning controllers, new tools for simulation of large scale quantum systems, and general applications of machine learning to various realizations of quantum computing platforms.
Dr. Justyna Zwolak
Dr. Natalia Ares
Prof. Eliska Greplova
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- automation of experiments
- scalability
- deep learning
- reinforcement learning
- autonomous tuning
- quantum computing
- qubit control
- quantum devices
