Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.

Tensor network methods provide a structured approach to representing and manipulating high-dimensional data by decomposing global information into interconnected low-rank tensors. Originating in the ...

Freek Witteveen, a senior researcher at CWI and QuSoft , has been selected for QDNL funding and mentorship through QDNL Top ...

Sam Mugel, Ph.D., is the CTO of Multiverse Computing, a global leader in developing value-driven quantum solutions for businesses. Carbon emissions continue to plague the planet’s climate and endanger ...

(A) Illustration of a convolutional neural network (NN) whose variational parameters (T) are encoded in the automatically differentiable tensor network (ADTN) shown in (B). The ADTN contains many ...

The future of the spatial economy is quite literally being built on the dust of the past. Scientists are working to scrunch AI models with tensor networks, a mathematical framework borrowed from ...

A hunk of material bustles with electrons, one tickling another as they bop around. Quantifying how one particle jostles others in that scrum is so complicated that, beginning in the 1990s, physicists ...

Dan Fleisch briefly explains some vector and tensor concepts from A Student’s Guide to Vectors and Tensors. In the field of machine learning, tensors are used as representations for many applications, ...