Quantum topology identification with deep neural networks and quantum walks

查看全文 作　　者：Yurui [1]Ming;Chin-Teng [1]Lin;Stephen [2]D.Bartlett;Wei-Wei [2]Zhang

机构地区：[1]Centre for Artificial Intelligence,School of Computer Science,University of Technology Sydney,Sydney,Australia;[2]Centre for Engineered Quantum Systems,School of Physics,The University of Sydney,Sydney,Australia

出　　处：《npj Computational Materials》索引2019年第1期,共7页

基　　金：This work is supported by the Australian Research Council via the Centre of Excellence in Engineered Quantum Systems project number CE170100009 and Discovery Project numbers DP170103073,DP180100670 and DP180100656,and USyd-SJTU Partnership Collaboration Awards. 摘　　要：Topologically ordered materials may serve as a platform for new quantum technologies,such as fault-tolerant quantum computers.To fulfil this promise,efficient and general methods are needed to discover and classify new topological phases of matter.We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions.On a trial topological ordered model,our method’s accuracy of topological phase identification reaches 97.4%,and is shown to be robust to noise on the data.Furthermore,we demonstrate that our trained DNN is able to identify topological phases of a perturbed model,and predict the corresponding shift of topological phase transitions without learning any information about the perturbations in advance.These results demonstrate that our approach is generally applicable and may be used to identify a variety of quantum topological materials. 关键词：QUANTUM TOPOLOGICAL TRANSITIONS

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Quantum topology identification with deep neural networks and quantum walks

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