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양자정보처리연구소 콜로퀴움_2022년 07월 21일 (목)_Hayata Yamasaki
Author
관리자
Date
2026-07-25 10:27
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4
- 날짜: 2022년 07월 21일 (목) 16:00 ~ 18:00
- 주제: 양자정보처리연구소 콜로퀴움
- 장소:
오프라인: 서울시립대학교 과학기술관 112호
온라인: ZOOM (https://uos-ac-kr.zoom.us/j/4894797497)
- 초청연사 -
Dr. Hayata Yamasaki (Affiliation. Institute for Quantum Optics and Quantum Information — IQOQI Vienna, Austrian Academy of Sciences)
Title Quantum Machine Learning: Basics of quantum computation and techniques for accelerating machine learning
Abstract
Recent technological advances open a way to apply quantum technologies to performing computation based on the law of quantum mechanics, which is called quantum computation. Quantum computation is believed to achieve significant speedups compared to conventional classical computation in solving a class of computational problems. However, it is usually challenging (yet exciting) to discover socially impactful problems for which we use such quantum speedups. In this talk, I discuss how we can use quantum computation to speed up machine-learning tasks. I will summarize a formulation of quantum computation and introduce several cutting-edge techniques for quantum machine learning.
The talk is based on theoretical developments and techniques in the following papers
https://arxiv.org/abs/2004.10756
https://arxiv.org/abs/2106.09028
- 주제: 양자정보처리연구소 콜로퀴움
- 장소:
오프라인: 서울시립대학교 과학기술관 112호
온라인: ZOOM (https://uos-ac-kr.zoom.us/j/4894797497)
- 초청연사 -
Dr. Hayata Yamasaki (Affiliation. Institute for Quantum Optics and Quantum Information — IQOQI Vienna, Austrian Academy of Sciences)
Title Quantum Machine Learning: Basics of quantum computation and techniques for accelerating machine learning
Abstract
Recent technological advances open a way to apply quantum technologies to performing computation based on the law of quantum mechanics, which is called quantum computation. Quantum computation is believed to achieve significant speedups compared to conventional classical computation in solving a class of computational problems. However, it is usually challenging (yet exciting) to discover socially impactful problems for which we use such quantum speedups. In this talk, I discuss how we can use quantum computation to speed up machine-learning tasks. I will summarize a formulation of quantum computation and introduce several cutting-edge techniques for quantum machine learning.
The talk is based on theoretical developments and techniques in the following papers
https://arxiv.org/abs/2004.10756
https://arxiv.org/abs/2106.09028
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