Neural networks and ‘ghost’ electrons accurately reconstruct the behavior of quantum systems

Neural networks and 'ghost' electrons accurately reconstruct the behavior of quantum systems

In a new approach to replicating quantum entanglement, additional “ghost” electrons are controlled by an artificial intelligence technique called a neural network. The network makes adjustments until it finds an accurate solution that can be projected back into the real world, recreating the effects of entanglement without the associated computational hurdles. Credit: Lucy Reading-Ikkanda/Simons Foundation

Physicists (temporarily) magnify reality to crack the code of quantum systems.

To predict the properties of a molecule or material, it must be the collective behavior of electrons. Such predictions could one day help researchers develop new drugs or design materials with sought-after properties such as superconductivity. The problem is that electrons can become “quantum-mechanically” entangled with each other, which means that they can no longer be treated individually. The entangled web of connections becomes absurdly difficult for even the most powerful computers to unravel instantly for any system with more than a handful of particles.

utilities, quantum physicists at the Center for Computational Quantum Physics (CCQ) at the Flatiron Institute in New York City and the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have circumvented the problem. They created a way to simulate entanglement by adding additional “ghost” electrons to their calculations that interact with the system’s actual electrons.

In the new approach, the behavior of the added electrons is controlled by an artificial intelligence technique called a neural network. The network makes adjustments until it finds an accurate solution that can be projected back into the real world, mimicking the effects of entanglement without the associated computational hurdles.

The physicists present their method August 3 in the Proceedings of the National Academy of Sciences.

“You can treat the electrons as if they weren’t talking to each other, as if they weren’t interacting,” said lead author Javier Robledo Moreno, a graduate student at CCQ and New York University. “The extra particles we add mediate the interactions between the real particles living in the physical system we’re trying to describe.”

Neural networks and 'ghost' electrons accurately reconstruct the behavior of quantum systems

An illustration of quantum entanglement. Credit: Lucy Reading-Ikkanda/Simons Foundation

In the new paper, the physicists show that their approach matches or is better than competing methods in simple quantum systems.

“We’ve applied this to simple things like a test bed, but now we’re taking this to the next step and trying this out molecules and other more realistic problems,” said study co-author and CCQ director Antoine Georges. “This is a big problem because if you have a good way of getting the wavefunctions of complex molecules, you can do all kinds of things , such as designing drugs and materials with specific properties.”

The long-term goal, Georges says, is to allow researchers to computationally predict the properties of a material or molecule without synthesizing and testing it in a lab. For example, they could be able to test a whole range of different molecules for a desired pharmaceutical property with just a few mouse clicks. “Simulating large molecules is a big problem,” Georges says.

Robledo Moreno and Georges co-authored the paper with EPFL assistant physics professor Giuseppe Carleo and CCQ researcher James Stokes.

The new work is an evolution of a paper from 2017 in Science by Carleo and Matthias Troyer, who is currently a technical fellow at Microsoft. That paper also combined neural networks with fictitious particles, but the added particles were not full-fledged electrons. Instead, they had only one trait known as spider.

“When I was [at the CCQ] in New York I was obsessed with the idea of ​​finding a version of neural network that would describe the way electrons behave, and I really wanted to find a generalization of the approach we introduced in 2017,” Carleo says. “With this new work, we finally found an elegant way to have hidden particles that don’t have spins. are just electrons.”


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More information:
Javier Robledo Moreno et al, Fermionic wavefunctions of neural network constrained hidden states, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2122059119

Provided by Simons Foundation

Quote: Neural networks and ‘ghost’ electrons accurately reconstruct the behavior of quantum systems (2022, August 3), retrieved August 4, 2022 from https://phys.org/news/2022-08-neural-networks-ghost-electrons- accurately.html

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