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 ...

One of the first steps toward becoming a scientist is discovering the difference between speed and velocity. To nonscientists, it’s usually a meaningless distinction. Fast is fast, slow is slow. But ...

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. The ...

Deep neural networks (NNs) encounter scalability limitations when confronted with a vast array of neurons, thereby constraining their achievable network depth. To address this challenge, we propose an ...

The quantum many body problem has been at the heart of much of theoretical and experimental physics over the past few decades. Even though we have understood the fundamental laws that govern the ...

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 ...

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.

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...