It is a critical challenge to simultaneously achieve high interpretability and high efficiency
with the current schemes of deep machine learning (ML). The tensor network (TN), a well …

The gradient descent approach is the key ingredient in variational quantum algorithms and
machine learning tasks, which is an optimization algorithm for finding a local minimum of an …

Quantum state tomography (QST) is plagued by the``curse of dimensionality''due to the
exponentially-scaled complexity in measurement and data post-processing. Efficient QST …

Given an image of a white shoe drawn on a blackboard, how are the white pixels deemed
(say by human minds) to be informative for recognizing the shoe without any labeling …

The constantly increasing dimensionality of artificial quantum systems demands for highly
efficient methods for their characterization and benchmarking. Conventional quantum …

Tensor networks offer a novel and powerful tool for solving a variety of problems in
mathematics, data science, and engineering. One such network is the multiscale …

Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating
low-rankness in the spectral domain over other tensor decompositions (like Tucker …

Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals
from relatively few measurements by exploiting known structures of signals of interest …

Algorithms developed to solve many-body quantum problems, like tensor networks, can turn
into powerful quantum-inspired tools to tackle problems in the classical domain. In this work …

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from
academic researches to clinical applications, such as in biomarkers' detection and …