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arXiv:1901.11103 [cond-mat.dis-nn]AbstractReferencesReviewsResources

Uncover the Black Box of Machine Learning Applied to Quantum Problem by an Introspective Learning Architecture

Ce Wang, Hui Zhai, Yi-Zhuang You

Published 2019-01-30Version 1

Recently there is an increasing interest in applying machine learning algorithm to physics problems. These applications provide new platforms to challenge the critical issue of how to uncover the black box of machine learning because there always exists well-defined rules under physics problems. The success of such efforts can offer great promise of discovering new physics from experimental data by artificial intelligence. As a benchmark and a proof-of-principle study that this approach is indeed possible, in this work we design an introspective learning architecture that can automatically develop the concept of the quantum wave function and discover the Schr\"odinger equation from simulated experimental data of the potential-to-density mappings of a quantum particle. This introspective learning architecture contains a translator and a knowledge distiller. The translator employs a recurrent neural network to learn the potential to density mapping, and the knowledge distiller applies the auto-encoder to extract the essential information and its update law from the hidden layer of the translator, which turns out to be the quantum wave function and the Schr\"odinger equation. We envision that our introspective learning architecture can enable machine learning to discover new physics in the future.

Comments: 5 pages, 6 figures + supplementary material
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