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Quantum computation requires large classical datasets to be embedded intoquantum states in order to exploit quantum parallelism. However, this embeddingrequires considerable resources. 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 allowslarge classical input data, with a relatively small-scale quantum system. Thishybrid architecture is used to implement an oracle. It is shown that in thepresence of noise in the hybrid oracle, the effects of internal noise cancancel each other out and thereby improve the query success rate. It is alsoshown that such an immunity of the hybrid oracle to noise directly and tangiblyreduces the sample complexity in the probably-approximately-correct learningframework. This NISQ-compatible learning advantage is attributed to theoracle's ability to handle large input features.

Tangible reduction in learning sample complexity with large classical samples and small quantum system