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

Quantum computation requires large classical datasets to be embedded into quantum states in order exploit parallelism. However, this embedding considerable resources. It would therefore desirable avoid it, if possible, for noisy intermediate-scale (NISQ) implementation. Accordingly, we consider a classical-quantum hybrid architecture, which allows input data, with relatively small-scale system. This architecture is used implement an oracle. shown that the presence of noise oracle, effects internal can cancel each other out and thereby improve query success rate. also such immunity oracle directly tangibly reduces sample complexity probably-approximately-correct learning framework. NISQ-compatible advantage attributed oracle's ability handle features.