Quantum-inspired algorithms using tensor networks can simulate turbulent fluid flows on classical computers much faster than existing methods. This approach reduces computation times from days on supercomputers to hours on regular laptops, potentially revolutionizing fields like weather forecasting and aircraft design.
Quantum-inspired algorithms can simulate turbulent fluid flows on a classical computer much faster than existing tools, slashing computation times from several days on a large supercomputer to just hours on a regular laptop. This could improveTurbulence in liquid or air involves numerous interacting eddies that quickly become so chaotically complex that precise simulation is impossible for even the.
Tensor networks originated in physics and came into common use in the early 2000s. They now offer a promising path to eke out much more performance from existing classical computers before truly useful quantum machines are available.“The algorithms and the way of thinking comes from the world of quantum simulation, and these algorithms are very close to what quantum computers do,” says Gourianov. “We’re seeing quite a drastic speed-up, both in theory and in practice.
Tensor networks work by, in effect, reducing the amount of data a simulation requires, drastically cutting the computational power required to run it. The amount and nature of the data removed can be carefully controlled by dialling the level of precision up or down.These mathematical tools have already been used in the cat-and-mouse game between quantum computer developers and classical computer scientists.
QUANTUM COMPUTING TURBULENCE SIMULATION CLASSICAL COMPUTERS ALGORITHMS TENSOR NETWORKS
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