Using machine learning tools to create a digital twin, or a virtual copy, of an electronic circuit that exhibits chaotic behavior, researchers found that they were successful at predicting how it would behave and using that information to control it.

Many everyday devices, like thermostats and cruise control, utilize linear controllers – which use simple rules to direct a system to a desired value. Thermostats, for example, employ such rules to determine how much to heat or cool a space based on the difference between the current and desired temperatures.

Yet because of how straightforward these algorithms are, they struggle to control systems that display complex behavior, like chaos.

As a result, advanced devices like self-driving cars and aircraft often rely on machine learning-based controllers, which use intricate networks to learn the optimal control algorithm needed to best operate. However, these algorithms have significant drawbacks, the most demanding of which is that they can be extremely challenging and computationally expensive to implement. 

Now, having access to an efficient digital twin is likely to have a sweeping impact on how scientists develop future autonomous technologies, said Robert Kent, lead author of the study and a graduate student in physics at The Ohio State University. 

“The problem with most machine learning-based controllers is that they use a lot of energy or power and they take a long time to evaluate,” said Kent. “Developing traditional controllers for them has also been difficult because chaotic systems are extremely sensitive to small changes.”

These issues, he said, are critical in situations where milliseconds can make a difference between life and death, such as when self-driving vehicles must decide to brake to prevent an accident.

The study was published recently in Nature Communications.

Compact enough to fit on an inexpensive computer chip capable of balancing on your fingertip and able to run without an internet connection, the team’s digital twin was built to optimize a controller’s efficiency and performance, which researchers found resulted in a reduction of power consumption. It achieves this quite easily, mainly because it was trained using a type of machine learning approach called reservoir computing. 

“The great thing about the machine learning architecture we used is that it’s very good at learning the behavior of systems that evolve in time,” Kent said. “It’s inspired by how connections spark in the human brain.”

Although similarly sized computer chips have been used in devices like smart fridges, according to the study, this novel computing ability makes the new model especially well-equipped to handle dynamic systems such as self-driving vehicles as well as heart monitors, which must be able to quickly adapt to a patient’s heartbeat.   

“Big machine learning models have to consume lots of power to crunch data and come out with the right parameters, whereas our model and training is so extremely simple that you could have systems learning on the fly,” he said. 

To test this theory, researchers directed their model to complete complex control tasks and compared its results to those from previous control techniques. The study revealed that their approach achieved a higher accuracy at the tasks than its linear counterpart and is significantly less computationally complex than a previous machine learning-based controller. 

“The increase in accuracy was pretty significant in some cases,” said Kent. Though the outcome showed that their algorithm does require more energy than a linear controller to operate, this tradeoff means that when it is powered up, the team’s model lasts longer and is considerably more efficient than current machine learning-based controllers on the market. 

“People will find good use out of it just based on how efficient it is,” Kent said. “You can implement it on pretty much any platform and it’s very simple to understand.” The algorithm was recently made available to scientists. 

Outside of inspiring potential advances in engineering, there’s also an equally important economic and environmental incentive for creating more power-friendly algorithms, said Kent. 

As society becomes more dependent on computers and AI for nearly all aspects of daily life, demand for data centers is soaring, leading many experts to worry over digital systems’ enormous power appetite and what future industries will need to do to keep up with it. 

And because building these data centers as well as large-scale computing experiments can generate a large carbon footprint, scientists are looking for ways to curb carbon emissions from this technology. 

To advance their results, future work will likely be steered toward training the model to explore other applications like quantum information processing, Kent said. In the meantime, he expects that these new elements will reach far into the scientific community. 

“Not enough people know about these types of algorithms in the industry and engineering, and one of the big goals of this project is to get more people to learn about them,” said Kent. “This work is a great first step toward reaching that potential.”

This study was supported by the U.S. Air Force’s Office of Scientific Research. Other Ohio State co-authors include Wendson A.S. Barbosa and Daniel J. Gauthier. 

The finding was made possible using the Relativistic Heavy Ion Collider (RHIC) at the Brookhaven National Laboratory in New York, which is capable of colliding gold ions at near light-speed. It led to the discovery of a new kind of quantum entanglement. 

The term quantum entanglement describes an invisible link that connects distant objects; no matter how far away they are in space, they affect each other. That means if two particles are entangled on a quantum level, by measuring the quantum state of one of the particles, you can immediately know the quantum state of the other, wherever it may be. For example, using a coin analogy, if one particle is “heads,” scientists instantly discern that the other particle is “tails,” no matter where in the universe it is.

Theoretical physicist Albert Einstein once dismissed the phenomenon of quantum entanglement as “spooky action at a distance,” but Daniel Brandenburg, co-author of the study and a professor of physics at The Ohio State University, said that learning more about this codependent relationship is fundamental to understanding the mysteries of the world around us. 

“Entanglement is one of the defining characteristics that makes quantum mechanics so different from the kind of physics that normally happens around us,” he said.  

The study of how photons and electrons interact with and affect matter, quantum mechanics is the foundation on which many technologies – such as quantum computing and quantum chemistry – are built. Despite these advancements, scientists previously believed that only particles of the same kind were capable of quantum interference: Photons could only interfere with photons and neutrons with neutrons. That is, until now. 

This new study, published in the journal Science Advances, describes how a team of researchers – called the STAR Collaboration – used the RHIC to uncover a form of quantum entanglement that shows that particles of all different kinds are able to interact with one another, leading to interference in a variety of different patterns. 

“We’ve gotten different kinds of particles to interfere for the first time, even though previously people thought that it wasn’t possible in quantum mechanics,” said Brandenburg. Using the collider like a large 3D digital camera, researchers used light to track the particles that escaped from the center of the machine once the atoms collided, taking high-resolution, two-dimensional images much like how a PET scan can be used to image and measure changes in the human body.

This method allowed researchers to map the arrangement of gluons – gluelike particles that act as a binding force for quarks, the particles within the protons and neutrons inside atomic nuclei. These interactions produced a subatomic particle called a pion that, by measuring the velocity and angles at which light struck the collider, researchers were able to essentially use as a microscope to see inside atomic nuclei in a way like never before. 

“By playing these quantum mechanical tricks, we can get to a precision which shouldn’t be possible otherwise,” Brandenburg said. “This precision allowed us to actually see, within an individual gold nucleus, where the protons and the neutrons reside.” 

This novel result was in part made thanks to a discovery Brandenburg made about two years ago, called the Breit-Wheeler process, which details how light can be turned into matter and antimatter. Building on the physics of this previous discovery, the team was able to view inside the nucleus on a scale of a tenth to a hundredth the size of an individual proton. 

“That’s mind-blowingly small,” said Brandenburg. 

The findings could eventually help advance research in several fields, from quantum computing to astrophysics, he said. 

Brandenburg, whose interest in nuclear physics originally began in astronomy, notes that because all matter is connected, investigating the inner workings of atomic nuclei could also allow astrophysicists to discern aspects like a star’s stability, its size, density, and even how it formed. “By doing this work here on Earth, we’re helping to actually understand better the things that are far out in the universe,” Brandenburg said. 

Going forward, the team hopes to extend its work by mapping the depths of other kinds of quantum objects.

“One of the big questions in our field is how do we understand the properties of this fundamental building block of matter,” he said. “With the discovery of this new type of entanglement, we can start to test these ideas for the first time.”

This work was supported by the Office of Nuclear Physics within the U.S. Department of Energy Office of Science, the U.S. National Science Foundation, the National Natural Science Foundation of China, and others.