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Journal Article
Tangible Reduction in Learning Sample Complexity with Large Classical Samples and Small Quantum System
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- Authors
- Wooyeong Song, Marcin Wie´sniak, Nana Liu, Marcin Pawłowski, Jinhyoung Lee, Jaewan Kim, Jeongho Bang
- Issue Date
- 2021-08
- Citation
- Quantum Information Processing, v.20, no.8, pp.1-18
- ISSN
- 1570-0755
- Publisher
- Springer
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Input features, Internal noise, Quantum Computation, Quantum states, Small-scale, Success rate, computational learning theory, hybrid architecture, input data, quantum system, sample complexity