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Traditional machine learning models ordinarily require substantial quantities of labeled data for top performance. Nevertheless, a single-shot learning seeks

Sounds and error correction: The precision and dependability of quantum machine learning models could be adversely afflicted by the inherent sounds and mistake-proneness of quantum systems. Considered one of the greatest challenges in the sphere is making economical error correction techniques.

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