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arXiv:1811.12630 · npj Computational Materials

Quantum topology identification with deep neural networks and quantum walks

Yurui Ming, Chin-Teng Lin, Stephen D. Bartlett, Weiwei Zhang

Published Aug 27, 2019• 42 citations
arXivHTMLPDF

Abstract

Abstract 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.

DOI
10.1038/s41524-019-0224-x

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Machine Learning (ML) techniques have emerged as a powerful tool in quantum matter research. In this poster, we highlight how ML algorithms enable us to analyze experimental data with unprecedented high sensitivities and identify topological phases even in the presence of unavoidable noises. To this end, we apply the trained network to low signal-to-noise-ratio (SNR) experimental data obtained in a symmetry-protected topological system of spin-orbit-coupled fermions [1]. The obtained phase diagram by ML is consistent with the results obtained by using the conventional method on higher SNR data. Our work highlights the potential of machine learning techniques to be used in various quantum systems.