|
 코드번호 : 36 |
|
| 발표자 : 지동표 |
|
| 소속 : 서울대학교 |
|
| 부서 : 수리과학부 |
|
| 직위 : 교수 |
|
| 세션시간 : 14:00~15:50 |
|
| 발표자약력 : |
- Univ. Pennsylvania 이학박사 (수학)
- 서울대학교 수리과학부 교수; (현) 명예교수
- UNIST 기초과정부 석좌교수
- MIT 교환교수 (1979-1980)
- 대한수학회 사업이사 |
|
|
| 강연요약 : |
Linear algebra is the basis of most of machine learning protocols. In 2009, Harrow, Hassidim, Lloyd developed so-called HHL algorithm which is a quantum linear algebra algorithm that outperforms classical algorithm so far developed, exponentially, at least in case of sparse matrix. Since then, many quantum machine learning algorithms are developed such as quantum principal component analysis, quantum support vector machine, and quantum topological data analysis toward NISQ regime. But many people have the opinion of usefulness against such algorithms, because we deal with classical data. In 2017, Kerenidis and Prakash developed quantum recommendation system when data are given in classical form. |
|
|
|
facebook