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

Understand

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.