It’s notoriously difficult to make sense of Quantum mechanics, and it’s equally difficult to calculate the behavior of many quantum systems. That’s due in part to the description of a quantum system called its wavefunction. The wavefunction for most single objects is pretty complicated on its own, and adding a second object makes predicting things even harder, since the wavefunction for the entire system becomes a mixture of the two individual ones. The more objects you add, the harder the calculations become.

As a result, many-body calculations are usually done through methods that produce an approximation. These typically involve either sampling potential solutions at random or figuring out some way to compress the problem down to something that can be solved. Now, though, two researchers at ETH Zurich, named Giuseppe Carleo and Matthias Troyer, have provided a third option: set a neural network loose on quantum mechanics.

Getting spooky