Abstract
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 atoms. Google's AlphaGo artificial neural network made headlines last year when it bested a world champion at Go. After marveling at this feat, Giuseppe Carleo of ETH Zurich in Switzerland thought it might be possible to build a similar tool to crack one of the knottiest problems in quantum physics: understanding many-body quantum systems. To assess this idea, Carleo and Matthias Troyer, now at Microsoft, built a simple neural network designed to reconstruct the wave function of a multi-body quantum system, or the set of probabilities describing how all the possible configurations could be arranged. They tested it on a few sample problems with known solutions and found it outperformed other tools.
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