Kavli Affiliate: Gang Su
| First 5 Authors: Shi-Ju Ran, Gang Su, , ,
| Summary:
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.
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