Physicists from the Center for Computational Quantum Physics at the Flatiron Institute taught a neural network to simulate
Predicting the properties of a molecule or materialrequires the calculation of the collective behavior of its electrons. The problem is that electrons can become “quantum-mechanically” entangled with each other, meaning they can no longer be handled individually, the authors explain. The tangled web of connections becomes incredibly difficult for even the most powerful computers to unravel directly for any system made up of more than a handful of particles.
To overcome this limitation, researchersuse a neural network that models the behavior of additional "ghost" electrons. These are particles that don't really exist, but that AI replaces quantum interactions with. The neural network corrects the behavior of these particles until it finds an exact solution that will fully correspond to real observations. It thus recreates the effects of entanglement without the attendant computational hurdles.
You can handle electrons likethey don't talk to each other like they don't interact with each other. The extra particles we add mediate interactions between real particles that live in the real physical system we are trying to describe.
Javier Robledo Moreno, researcher at New York University, co-author of the paper
In their article, the researchers demonstratedthe effectiveness of the method for modeling the wave functions of "entangled" electrons for simple systems that can be calculated by other methods. The results of the simulation of quantum entanglement completely coincided with alternative calculations.
The researchers believe that the new method will allowpredict the properties of a material or molecule without having to synthesize and test them in the laboratory. For example, it will be possible to test many different molecules for a desired pharmaceutical property with just a few mouse clicks.
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