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 ...
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 take a central role in quantum physics as they can provide an efficient approximation to specific classes of quantum states. The associated graphical language can also easily describe ...
(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 ...
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 ...
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 ...
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 ...
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