  It is a critical challenge to simultaneously gain high interpretability and
efficiency with the current schemes of deep machine learning (ML). Tensor
network (TN), which is a well-established mathematical tool originating from
quantum mechanics, has shown its unique advantages on developing efficient
``white-box'' ML schemes. Here, we give a brief review on the inspiring
progresses made in TN-based ML. On one hand, interpretability of TN ML is
accommodated with the solid theoretical foundation based on quantum information
and many-body physics. On the other hand, high efficiency can be rendered from
the powerful TN representations and the advanced computational techniques
developed in quantum many-body physics. With the fast development on quantum
computers, TN is expected to conceive novel schemes runnable on quantum
hardware, heading towards the ``quantum artificial intelligence'' in the
forthcoming future.
