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 ...

Google’s system leverages optical circuit switching (OCS) to create direct, low-latency optical paths between TPU chips, minimizing signal conversion losses. They avoid repeated ...

(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 ...

This review represents a strategic blueprint shaped by the world’s leading quantum experts. We believe that solving the most complex challenges requires collective intelligence and collaboration.” — ...

Although OpenAI says that it doesn’t plan to use Google TPUs for now, the tests themselves signal concerns about inference costs. OpenAI has begun testing Google’s Tensor Processing Units (TPUs), a ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. You may have access to this article through your institution.

The study of many-body quantum systems out of equilibrium remains a significant challenge, with complexity barriers arising in both state- and operator-based representations. Here, we review the ...

Advancements in neural networks have brought significant changes across domains like natural language processing, computer vision, and scientific computing. Despite these successes, the computational ...

Abstract: Tensor networks are a popular and computationally efficient approach to simulate general quantum systems on classical computers and, in a broader sense, a framework for dealing with ...