Quantum-inspired algorithm improves weather forecasting.

The algorithm inspired by quantums allows you to simulate the turbulent liquid flow on a classic computer much faster than the existing tools, and calculate from a few days of a large supercomputer to a normal laptop. Can be reduced. Researchers say that the weather forecast can be improved and industrial processes can be improved.

Liquid or air turbulence has a lot of interactions and quickly becomes very complicated, so it is impossible for the most powerful computer to simulate accurately. The quantum counter part promises to improve the problem, but now the most advanced machine cannot do anything other than rudimentary demonstrations.

These turbulent simulations can be simplified by replacing accurate calculations with probability. However, even with this approximation, scientists will surely request scientists to solve them.

Nikita Guulianov Oxford University and his colleagues have now developed a new approach to the stream probability distribution using algorithms inspired by quantum computers called Tensol Network.

Tensol networks were derived from physics and were commonly used in the early 2000s. They now provide a promising path to show much more performance from existing classical computers before truly convenient quantum machines become available.

“Algorithms and ideas come from the world of quantum simulation. These algorithms are very close to the quantum computer,” says Gourianov. “Both the theory and the actual can see a very dramatic speed up.”

In just a few hours, the team was able to perform a simulation on a laptop that took several days on a supercomputer before. With the new algorithm, the demand for processors has decreased by 1000 times and memory demand has decreased by 1 million times. This simulation was just a test, but the same type of problem is behind the weather, aircraft analysis, and industrial chemistry analysis.

It is said that the turbulent problem with five dimensions data is very difficult without using the tensor. Gunner Meller At Kent University. “It's a nightmare in calculation,” he says. “If you have a super computer and are happy to run for 1-2 months, you can do it in a limited case.”

The tensor network actually works by reducing the amount of data required for simulation and greatly reducing the calculation capacity required to execute it. The amount and nature of the deleted data can be carefully controlled by dialing the upper and lower accuracy level.

These mathematics tools are already used in cats and mouse games between quantum computer developers and classic computer scientists. Google announced in 2019 that a quantum processor called Sycamore has achieved “quantum advantage.” This is a point where quantum computers can complete tasks that are impossible for regular computers for all intentions and purposes.

However, the Tensol network, which simulates the same problem with a large -scale cluster of a conventional graphic processing unit, later achieved the same thing over 14 seconds and lost its previous claim. Since then, Google has once again pulled a new WILLOW Quantum Machine.

When a large -scale and fault -resistant quantum computer is created, the tensor can be executed on a much larger scale than the classic computer, but Möller is excited about what may be achieved in the meantime. I say you are.

“If you use a laptop, the author of this paper may lose what you can do with a supercomputer. You can get a big profit right away and have a perfect quantum computer.