Only a few states in high-dimensional systems can be identified as (un)steerable using existing theoretical or experimental methods. We utilize semidefinite programming (SDP) to construct a dataset for steerability detection in qutrit-qutrit systems. For the full-information feature F1, artificial neural networks achieve high classification accuracy and generalization, and perform better than the support vector machine. As feature engineering plays a pivotal role, we introduce a steering ellipsoidlike feature, F2, which significantly enhances the performance of each of our models. To address quantum steering detection in isotropic states, partially entangled states, and random states, we explore, respectively, the most tailored feature and classifier. Given that the SDP method provides only a sufficient condition for steerability detection, we establish the first rigorously constructed, accurately labeled dataset based on theoretical foundations. This dataset enables models to exhibit outstanding accuracy and generalization capabilities, independent of the choice of features. As applications, we investigate the steerability boundaries of isotropic states and partially entangled states, discover new steerable states, and determine their parameter ranges. This work not only advances the application of machine learning for probing quantum steerability in high-dimensional systems but also deepens the theoretical understanding of quantum steerability itself.

孟会贤，华北电力大学数理学院副教授，2017年6月博士毕业于陕西师范大学，获基础数学博士学位，目前主要从事量子信息与量子人工智能方面的研究。2017年7月至2019年6月在南开大学陈省身数学所做博后。主持国家自然科学基金一项（已结题），中国博士后科学基金面上资助项目两项（已结题），参与国家自然科学基金面上项目一项（在研）。博士学位论文获陕西省优秀博士学位论文。在Physical Review Letters 以共同第一作者身份发表一篇论文，在Physical Review A 以通讯作者/第一作者/共同第一作者身份发表五篇论文。共发表SCI论文17篇。