Learning quantum properties from short-range correlations using multi-task networks.

ABSTRACT: Characterizing multipartite quantum systems is crucial for quantum computing and many-body physics. The problem, however, becomes challenging when the system size is large and the properties of interest involve correlations among a large number of particles. Here we introduce a neural network model that can predict various quantum properties of many-body quantum states with constant correlation length, using only measurement data from a small number of neighboring sites. The model is based on the technique of multi-task learning, which we show to offer several advantages over traditional single-task approaches. Through numerical experiments, we show that multi-task learning can be applied to sufficiently regular states to predict global properties, like string order parameters, from the ob

SUBMITTER: Wu YD 

PROVIDER: S-EPMC11467203 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature