Descripción del proyecto
Redes neuronales artificiales que nos ayudan a conocer la dinámica cuántica de muchos cuerpos
Uno de los cometidos clave de la teoría cuántica de muchos cuerpos es la identificación de comportamientos universales en la materia cuántica. En los últimos años, la búsqueda de fases con nuevas propiedades universales se ha visto revolucionada al forzar desequilibrios en los sistemas, lo cual ha abierto un universo de fenómenos sin explorar y nuevos paradigmas dinámicos. Sin embargo, la descripción teórica de tales estados cuánticos de desequilibrio sigue constituyendo un importante desafío. El objetivo principal del proyecto mlQuDyn, financiado con fondos europeos, es avanzar en esa intrigante frontera aplicando un planteamiento interdisciplinar en la interfaz entre la teoría cuántica de muchos cuerpos y el aprendizaje automático. Los nuevos conocimientos obtenidos por este programa de investigación no solo aportarán información nueva sobre cuestiones pendientes de gran importancia relacionadas con la comprensión teórica de los sistemas cuánticos de muchos cuerpos, sino que también mejorarán el poder predictivo de la teoría cuántica en los experimentos.
Objetivo
A key scope of quantum many-body theory is the identification of universal behavior in quantum matter, where macroscopic properties become independent of microscopic details. In recent years the quest for phases with novel universal properties has been revolutionized by forcing systems out of equilibrium, which has opened up a universe of unexplored phenomena and new dynamical paradigms. These developments not only hold the promise to theoretically uncover unrecognized universal dynamical behavior, but are also driven by the enormous advances in quantum simulators such as ultra-cold atoms, which have nowadays achieved unique capabilities in generating and probing such nonequilibrium quantum states. Still, their theoretical description is facing severe challenges. It is the aim of this proposal to take the theoretical understanding and predictive power of quantum many-body theory to a new level by an crossdisciplinary approach at the interface between quantum dynamics and machine learning.
The central element of this approach is to encode time-evolved quantum states into artificial neural networks, which have been remarkably successful in storing and recognizing complex structures in computer science. In order to reach the main goal we have identified three main challenges which form the core of the program: (i) to design efficient artificial network structures based on fundamental principles of quantum many-body systems such as locality and causality; (ii) to utilize concepts of many-body theory and statistical physics to understand the physical properties of artificial neural networks; (iii) to explore fundamental but yet inaccessible dynamical quantum phenomena and universal behavior in quantum dynamics. The successfully conducted research program will lift the description and understanding of quantum many-body dynamics to a new level, impacting significantly both quantum theory as well as future experiments.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
ERC-STG - Starting GrantInstitución de acogida
86159 Augsburg
Alemania