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简介：吴亚东，2016年本科毕业于中国人民大学物理系。现在就读于清华大学高等研究院，在翟荟老师课题组博士生在读，主要做机器学习和物理学相关的内容，量子机器学习等。
摘要：Machine learning based on neural networks has recently provided significant advances for many practical applications. In physics, one natural application is the study of the quantum many-body systems. There are several works about using quantum computation or quantum neural networks to enhance conventional machine learning tasks. In this work, we use two qualities of a quantum circuit to describe the expression ability of a quantum circuit structure. One is the tripartite information, other is the operator size. We build a few different kinds of quantum circuit structures and compare the learning ability of these structures. After learning a quantum task and a classical task, we find the more scramble of a circuit, the better it can learn. What’s more, we design ‘the super circuit’, which has the best learning ability.
