  Machine learning has achieved success in many areas because of its powerful
fitting ability, so we hope it can help us to solve some significant physical
quantitative problems, such as quantum correlation. In this research we will
use neural networks to predict the value of quantum discord. Quantum discord is
a measure of quantum correlation which is defined as the difference between
quantum mutual information and classical correlation for a bipartite system.
Since the definition contains an optimization term, it makes analytically
solving hard. For some special cases and small systems, such as two-qubit
systems and some X-states, the explicit solutions have been calculated.
However, for general cases, we still know very little. Therefore, we study the
feasibility of estimating quantum discord by machine learning method on
two-qubit systems. In order to get an interpretable and high performance model,
we modify the ordinary neural network by introducing some prior knowledge which
come from the analysis about quantum discord. Our results show that prior
knowledge actually improve the performance of neural network.
