Physics

Transition role of entangled data in quantum machine learning

X. Wang, Y. Du, et al.

This groundbreaking study by Xinbiao Wang, Yuxuan Du, Zhuozhuo Tu, Yong Luo, Xiao Yuan, and Dacheng Tao explores the fascinating effects of entangled training data on quantum machine learning models. It reveals that while increased entanglement can reduce prediction error with sufficient measurements, too much entanglement with limited measurements can lead to unexpected prediction errors. This insight is vital for developing quantum machine learning protocols for early quantum computers.