Quantum enhanced machine learning
- Submission status
- Closed
- Submission deadline
Machine learning, and more generally, artificial intelligence, has achieved dramatic success over the past decade. This has been apparent in the tackling of notoriously challenging problems such as mastering the game of Go or predicting protein structures. Meanwhile, the quantum computing field has also undergone impressive development, with the experimental demonstration of quantum supremacy and error correction codes marked as the latest breakthroughs. These two rapidly advancing fields have now intersected, giving birth to a new research frontier: quantum machine learning.
In recent years, a number of new quantum algorithms, which hold the unprecedented potential to enhance, speed up or innovate machine learning, have been proposed, and some of them have even been demonstrated in the laboratory. Noteworthy examples include the Harrow-Hassidim-Lloyd algorithm, quantum principal component analysis, quantum classifiers, quantum generative models, and quantum adversarial learning. Given the exciting news that the Nobel Prize in Physics 2024 has been awarded to John J. Hopfield and Geoffrey E. Hinton, who “used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning”, we expect quantum computing to spark another leap for machine learning and artificial intelligence in the near future.
This collection welcomes Research Articles or Reviews on the following topics, including but not limited to:
- Quantum learning algorithms
- Quantum learning advantages
- Quantum generative models
- Quantum language processing
- Quantum adversarial learning
- Quantum federated learning
Editors
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Dong-Ling Deng, PhD
Tsinghua University, China
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Bei Zeng, PhD
The University of Texas at Dallas, USA
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Lei Wang, PhD
Institute of Physics, Chinese Academy of Sciences, China
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Junyu Liu, PhD
University of Pittsburg, USA
