Quantum Machine Learning (QML)
- Submission status
- Open
- Submission deadline
Quantum Machine Learning is an interdisciplinary field that harnesses the computational power of quantum systems to develop algorithms that can process and analyze data more efficiently than classical machine learning algorithms. Quantum systems can represent and process information in high-dimensional feature spaces using fewer resources than classical systems. By encoding data into quantum states, researchers can exploit the vast quantum feature space for machine learning tasks. Quantum algorithms can perform operations like matrix multiplication and vector dot products exponentially faster, using techniques like quantum Fourier transform and quantum phase estimation. Moreover, quantum annealing, leveraging quantum tunneling and superposition to find the global minimum of a function, can be used to speed up the processing of optimization problems.
This collection is dedicated to research endeavors aimed at overcoming challenges related to noise and error correction in quantum systems, improving the scalability of quantum algorithms, and identifying practical applications where quantum machine learning can provide significant advantages.
Editors
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Prasanna Date, PhD
Oak Ridge National Laboratory, USA
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Gabriel A. Silva, MSc, PhD
University of California San Diego, USA
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Snekhalatha Umapathy, M,E PhD
SRM Institute of Science and Technology, India
