We highlight a seminal contribution to quantum machine learning titled:
Learning the N-Input Parity Function with a Single-Qubit and Single-Measurement Sampling
This work addresses the problem of learning parity functions, a well-known hard task in classical learning theory. The authors show that a highly resource-constrained quantum model—using only one qubit and a single measurement per sample—can efficiently learn the N-input parity function.
The study provides a clear theoretical separation between classical and quantum learning capabilities, emphasizing that quantum advantage can arise even with minimal quantum hardware. These findings are particularly relevant for near-term quantum devices and foundational research in quantum learning theory.
🔗 Full paper available at:
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.200501