# Machine Learning Physics: From Quantum Mechanics to Holographic Geometry

### APA

You, Y. (2019). Machine Learning Physics: From Quantum Mechanics to Holographic Geometry. Perimeter Institute. https://pirsa.org/19070014

### MLA

You, Yi-Zhuang. Machine Learning Physics: From Quantum Mechanics to Holographic Geometry. Perimeter Institute, Jul. 11, 2019, https://pirsa.org/19070014

### BibTex

@misc{ pirsa_PIRSA:19070014, doi = {10.48660/19070014}, url = {https://pirsa.org/19070014}, author = {You, Yi-Zhuang}, keywords = {Condensed Matter}, language = {en}, title = {Machine Learning Physics: From Quantum Mechanics to Holographic Geometry}, publisher = {Perimeter Institute}, year = {2019}, month = {jul}, note = {PIRSA:19070014 see, \url{https://pirsa.org}} }

University of California, San Diego

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Abstract

Inspired by the "third wave" of artificial intelligence (AI), machine learning has found rapid applications in various topics of physics research. Perhaps one of the most ambitious goals of machine learning physics is to develop novel approaches that ultimately allows AI to discover new concepts and governing equations of physics from experimental observations. In this talk, I will present our progress in applying machine learning technique to reveal the quantum wave function of Bose-Einstein condensate (BEC) and the holographic geometry of conformal field theories. In the first part, we apply machine translation to learn the mapping between potential and density profiles of BEC and show how the concept of quantum wave function can emerge in the latent space of the translator and how the Schrodinger equation is formulated as a recurrent neural network. In the second part, we design a generative model to learn the field theory configuration of the XY model and show how the machine can identify the holographic bulk degrees of freedom and use them to probe the emergent holographic geometry.