Tensor networks for interpretable and efficient quantum-inspired machine learning
This work tackles the problem of interpretability and efficiency in machine learning for researchers and practitioners, but it is incremental as it reviews existing progress rather than introducing new methods.
The paper reviews tensor network (TN) methods for machine learning, addressing the challenge of achieving both high interpretability and efficiency by leveraging quantum-inspired mathematical tools. It highlights TN's potential for developing efficient 'white-box' ML schemes and its future applicability on quantum hardware.
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.