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UID:submissions.pasc-conference.org_PASC24_sess131_msa239@linklings.com
SUMMARY:Towards High-Performant, Large-Scale Tensor Network Simulations
DESCRIPTION:Minisymposium\n\nSergio Sánchez Ramírez (Barcelona Supercomput
 ing Center)\n\nTensor Networks are in the center of every disproval of qua
 ntum advantage claims in these recent years. Achieving quantum advantage, 
 the predicted theoretical moment when quantum computers will beat classica
 l computers on some problems, is key for the general adoption of quantum c
 omputers. But proving it theoretically has proven to be a task of great di
 fficulty. Due to that, researchers have tried to attack the problem from a
 n empirical point of view, by finding a problem which is solvable in a qua
 ntum computer, but intratable in a supercomputer. Tensor Networks are a ma
 thematical framework for multilinear operators which originated in the Con
 densed Matter community but have also proven to be very powerful in Quantu
 m Information/Computation and Machine Learning tasks. In this talk, I will
  speak about our efforts on developing a modular Tensor Network and Quantu
 m Simulation framework for high-performant and large-scale simulations in 
 some of the top supercomputers in the world.\n\nDomain: Climate, Weather, 
 and Earth Sciences, Physics, Computational Methods and Applied Mathematics
 \n\nSession Chairs: Samuel Omlin (ETH Zurich / CSCS); Ludovic Raess (Unive
 rsity of Lausanne, ETH Zurich); and Michael Schlottke-Lakemper (University
  of Augsburg)
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