Learning quantum properties from short-range correlations using multi-task networks
This work addresses the problem of efficient quantum system characterization for quantum computing and many-body physics, offering a novel method that improves over traditional approaches.
The authors tackled the challenge of characterizing large multipartite quantum systems by introducing a neural network model that predicts various quantum properties from short-range correlations, showing it can predict global properties like string order parameters and distinguish quantum phases using multi-task learning.
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 observation of short-range correlations, and to distinguish between quantum phases that cannot be distinguished by single-task networks. Remarkably, our model appears to be able to transfer information learnt from lower dimensional quantum systems to higher dimensional ones, and to make accurate predictions for Hamiltonians that were not seen in the training.