Computer Science

Generalization in quantum machine learning from few training data

M. C. Caro, H. Huang, et al.

Explore the groundbreaking findings of Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J. Coles as they unveil how Quantum Machine Learning can significantly enhance generalization performance even with sparse training data. Their study reveals the remarkable scalability of generalization error, offering exciting prospects for quantum applications like unitary compiling and error correction.