An Overview of Quantum Machine Learning Research in China
Source abstract
Abstract
Quantum machine learning (QML) is an emerging discipline that combines quantum computing and machine learning and is able to exhibit exponential superiority over classical machine learning regarding computing speed on specific problems. This article provides a comprehensive review of the QML research in China. The QML development in China is presented in terms of research ideas and tasks, and the algorithms and application fields are sorted out. We have also highlighted some typical creative studies and illuminated their innovation points. Furthermore, the current challenges and future prospects are discussed. This review may provide inspiration for both China’s and global QML-domain progress.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Quantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
University of Chinese Academy of Sciences, Beijing 100049, China
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China
Funding
National Natural Science Foundation of China (NSFC) · National Key R&D Program of China · Shanghai Rising-Star Program · Natural Science Foundation of Shanghai
References (100+)
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