基于机器学习探测qubit-qutrit系统中量子态的导引性

Steerability detection of quantum states in qubit-qutrit systems via machine learning

受机器学习研究2-qubit系统上量子态导引性探测的启发,本文利用不同的机器学习方法研究qubit-qutrit系统上量子态导引性的探测,发现:(1)对于随机态、Werner态和UN态(一类新构造的Alice不可导引Bob的纠缠态)都有导引性探测准确率达到95%以上的监督或半监督机器学习方法;(2)由监督机器学习方法预测的导引界,大部分高于理论提供的不可导引界,低于半正定规划算法(SDP)确定的导引界,这表明机器学习方法对qubit-qutrit系统中量子态导引性探测具有可靠性,且较SDP方法可探测到更多的导引态,为利用机器学习方法探测两体高维系统中量子态导引性奠定基础.

Inspired by the research on detecting the steerability of two-qubit states via machine learning,this study explores the detection of steerability in qubit-qutrit systems using various machine learning methods.It is found that,(1)for random states,Werner states,and UN states(a newly constructed class of entangled states where Alice cannot steer Bob),there always exist supervised or semi-supervised machine learning methods with a steering detectability accuracy rate of over 95%;(2)the steering bounds predicted by the supervised machine learning methods are mostly higher than the nonsteerable bounds provided by theory and lower than the steering bounds determined by the semidefinite programming(SDP).This demonstrates that the machine learning methods are reliable for detecting the steerability of quantum states in qubit-qutrit systems and can detect more steering states compared with the SDP method.It lays the foundation for utilizing machine learning methods to detect quantum states’steerability in bipartite and high-dimensional systems.