Jennifer Oullette in New Scientist:
The same type of artificial intelligence that mastered the ancient game of Go could help wrestle with the amazing complexity of quantum systems containing billions of particles.
Google’s AlphaGo artificial neural network made headlines last year when it bested a world champion at Go. After marvelling at this feat, Giuseppe Carleo of ETH Zurich in Switzerland thought it might be possible to build a similar machine-learning tool to crack one of the knottiest problems in quantum physics.
Now, he has built just such a neural network – which could turn out to be a game changer in understanding quantum systems.
Go is far more complex than chess, in that the number of possible positions on a Go board could exceed the number of atoms in the universe. That’s why an approach based on brute-force calculation, while effective for chess, just doesn’t work for Go.
In that sense, Go resembles a classic problem in quantum physics: how to describe a quantum system that consists of many billions of atoms, all of which interact with each other according to complicated equations.