Tangible reduction in learning sample complexity with large classical samples and small quantum systemopen access
- Authors
- Song, Wooyeong; Wiesniak, Marcin; Liu, Nana; Pawlowski, Marcin; Lee, Jinhyoung; Kim, Jaewan; Bang, Jeongho
- Issue Date
- Aug-2021
- Publisher
- SPRINGER
- Keywords
- Quantum machine learning; Probably Approximately Correct (PAC) learning; Classical-quantum hybrid query; Sample complexity
- Citation
- QUANTUM INFORMATION PROCESSING, v.20, no.8, pp.1 - 18
- Indexed
- SCIE
SCOPUS
- Journal Title
- QUANTUM INFORMATION PROCESSING
- Volume
- 20
- Number
- 8
- Start Page
- 1
- End Page
- 18
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141329
- DOI
- 10.1007/s11128-021-03217-7
- ISSN
- 1570-0755
- Abstract
- Quantum computation requires large classical datasets to be embedded into quantum states in order to exploit quantum parallelism. However, this embedding requires considerable resources in general. It would therefore be desirable to avoid it, if possible, for noisy intermediate-scale quantum (NISQ) implementation. Accordingly, we consider a classical-quantum hybrid architecture, which allows large classical input data, with a relatively small-scale quantum system. This hybrid architecture is used to implement a sampling oracle. It is shown that in the presence of noise in the hybrid oracle, the effects of internal noise can cancel each other out and thereby improve the query success rate. It is also shown that such an immunity of the hybrid oracle to noise directly and tangibly reduces the sample complexity in the framework of computational learning theory. This NISQ-compatible learning advantage is attributed to the oracle's ability to handle large input features.
- Files in This Item
-
- Appears in
Collections - 서울 자연과학대학 > 서울 물리학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/141329)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.