동향

양자컴퓨팅 & 양자머신러닝 연구의 현재와 미래

분야

전기/전자

발행기관

한국전자통신연구원

발행일

2023-10-01

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Quantum machine learning (QML) is an area of quantum computing that leverages its principles to develop machine learning algorithms and techniques. QML is aimed at combining traditional machine learning with the capabilities of quantum computing to devise approaches for problem solving and (big) data processing. Nevertheless, QML is in its early stage of the research and development. Thus, more theoretical studies are needed to understand whether a significant quantum speedup can be achieved compared with classical machine learning. If this is the case, the underlying physical principles may be explained. First, fundamental concepts and elements of QML should be established. We describe the inception and development of QML, highlighting essential quantum computing algorithms that are integral to QML. The advent of the noisy intermediate-scale quantum era and Google’s demonstration of quantum supremacy are then addressed. Finally, we briefly discuss research prospects for QML.

Ⅰ. 양자컴퓨팅: 과거와 현재
Ⅱ. 양자머신러닝 연구
Ⅲ. 결론
용어해설
약어 정리
각주

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