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 ...

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.

Physicists have long been fascinated by the strange behavior of matter at a second-order phase transition, the razor-thin ...

Tensor networks enable researchers to tackle quantum physics problems previously thought to be solvable only by quantum computers. Lucy Reading-Ikkanda/Simons Foundation Using a conventional computer ...

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 ...

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 ...

Drones are quickly becoming the flying workhorses of the wireless world. They relay video from disaster zones, ferry ...

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 ...

(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 ...