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 ...
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 ...
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 ...
Tensor networks pictographic scheme Each tensor is represented by an array of dimension N, that matches its number of indices. By joining the legs (indices) of each tensor, it is possible to encode ...
An extension of tensor networks—mathematical tools that simplify the study of complex quantum systems—could allow their application to a broad range of quantum field theory problems. Figure 1: Tilloy ...
(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 ...
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 ...
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 ...
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