Scientists extend reality to crack the code of quantum systems

Researchers precisely reconstruct the behavior of quantum systems using neural networks and “ghost” electrons.

A new method for simulating quantum entanglement between interacting particles has been developed by physicists.

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

Calculating the collective behavior of a molecule’s electrons is necessary to predict material properties. Such predictions could one day help scientists create new drugs or materials with desirable properties such as superconductivity. The point is that electrons can become “quantum mechanically” entangled with each other, meaning they can no longer be treated individually. For any system with more than a few particles, the tangled web of connections becomes prohibitively difficult to untangle directly even by the most powerful computers.

Now, quantum physicists at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York have found a workaround. By adding extra “ghost” electrons into their calculations that interact with the system’s actual electrons, they were able to simulate entanglement.

Reproducing quantum entanglement

An infographic describing the process. Credit: Lucy Reading-Ikkanda/Simons Foundation

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, thereby recreating the effects of entanglement without the attendant computational hurdles.

The scientists recently published their work in the journal Proceedings of the National Academy of Sciences.

“You can treat electrons as if they don’t talk to each other, as if they don’t interact,” says lead study author Javier Robledo Moreno, a graduate student at CCQ and[{” attribute=””>New York University. “The extra particles we’re adding are mediating the interactions between the actual ones that live in the actual physical system we’re trying to describe.”

In the new paper, the physicists demonstrate that their approach matches or outclasses competing methods in simple quantum systems.

“We applied this to simple things as a test bed, but now we are taking this to the next step and trying this on molecules and other, more realistic problems,” says study co-author and CCQ director Antoine Georges. “This is a big deal because if you have a good way of getting the wave functions of complex molecules, you can do all sorts of things, like designing drugs and materials with specific properties.”

The long-term goal, Georges says, is to enable researchers to computationally predict the properties of a material or molecule without having to synthesize and test it in a lab. They might, for instance, be able to test a slew of different molecules for a desired pharmaceutical property with just a few clicks of a mouse. “Simulating big molecules is a big deal,” Georges says.

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

The new work is an evolution of a 2017 paper 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 weren’t full-blown electrons. Instead, they just had one property known as spin.

“When I was [at the CCQ] in New York, I was obsessed with the idea of ​​finding a neural network version to 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 are not spins but electrons.”

Reference: “Fermionic wavefunctions from hidden states constrained by neural networks” by Javier Robledo Moreno, Giuseppe Carleo, Antoine Georges, and James Stokes, 3 Aug 2022, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2122059119

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