Emergent Quantum Mechanics in an Introspective Machine Learning Architecture

Abstract

Can physical concepts and laws emerge in a neural network as it learns to predict the observation data of physical systems? As a benchmark and a proof-of-principle study of this possibility, here we show an introspective learning architecture that can automatically develop the concept of the quantum wave function and discover the Schrödinger equation from simulated experimental data of the potential-to-density mappings of a quantum particle. This introspective learning architecture contains a machine translator to perform the potential to density mapping, and a knowledge distiller auto-encoder to extract the essential information and its update law from the hidden states of the translator, which turns out to be the quantum wave function and the Schrödinger equation. We envision that our introspective learning architecture can enable machine learning to discover new physics in the future.

5 pages, 6 figures + supplementary material

References in corpus (7)

• Learning phase transitions by confusion
• Unsupervised Learning of Frustrated Classical Spin Models I: Principle Component Analysis
• Pure density functional for strong correlations and the thermodynamic limit from machine learning
• Machine Learning in the String Landscape
• Deep Learning and AdS/CFT
• Machine Learning Spatial Geometry from Entanglement Features
• Machine Learning of Frustrated Classical Spin Models. II. Kernel Principal Component Analysis

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