It’s difficult to simulate quantum physics, as the computing demand grows exponentially the more complex the quantum system gets — even a supercomputer might not be enough. AI might come to the rescue, though. Researchers have developed a computational method that uses neural networks to simulate quantum systems of “considerable” size, no matter what the geometry. To put it relatively simply, the team combines familiar methods of studying quantum systems (such as Monte Carlo random sampling) with a neural network that can simultaneously represent many quantum states.
The appeal is easy to grasp, at least: quantum physicists could study complex systems without needing massive amounts of computing power. That could help scientists understand more aspects of quantum behavior. The technique might be particularly helpful for developing quantum computers, where it could determine the effects of noise on the hardware. All told, this should nudge quantum computing one step closer toward the mainstream.
