Exploring the Power of Entangled Data in Quantum Machine Learning

Quantum entanglement is a key resource for achieving superiority of quantum computing.Currently,scientists are extensively focusing on how to integrate quantum entanglement into various components of quantum machine learning(QML)models,aiming to surpass the performance of traditional machine learning models.Notable successes include the use of entangled measurements^([1-3])and entangled channels^([4]),which have been shown to reduce query complexity or improve the prediction precision for specified QML tasks.Quantum entangled data,capable of encoding more information compared to classical data of the same size,is recognized for its potential to achieve quantum advantages.Nevertheless,the impact of the entanglement degree in quantum data on model performance remains a challenging and unresolved research question.