Using a new combination of materials, scientists have developed a so-called "quantum brain," a piece of computer hardware capable of physically reconfiguring itself as it processes and stores information.

Like the human brain, the new hardware -- described Monday in the journal Nature Nanotechnology -- physically reorganizes itself as it learns.

Modern computers rely on machine learning software to recognize information processing patterns and develop new strategies for storing information on a separate hard drive.

"Until now, this technology, which is based on a century-old paradigm, worked sufficiently," study co-author Bert Kappen, professor of neural networks and machine intelligence at Radboud University in the Netherlands, said in a news release. "However, in the end, it is a very energy-inefficient process."

As the digital economy grows and more and more people spend more and more time online, the strain on the Internet's infrastructure -- and the energy grid -- increases.

To shrink the digital economy's carbon footprint, scientists are searching for ways to make data storage and processing centers more efficient.

Quantum computing is one solution, and researchers at Radboud said their new quantum brain hardware learn and adapt without the assistance -- and baggage -- of software.

The new hardware is composed of a network of cobalt atoms deposited onto black phosphorus.

Previous studies showed that single cobalt atoms are capable of storing bits of information. In computers, information exists as binary values of 0 and 1. Previous tests showed that when cobalt atoms are subjected to a voltage of electricity, they throttle between two electronic states, or between values of 0 and 1, like a neuron in the brain.

More recently, researchers were able to configure a network of cobalt atoms, through which information could flow. Researchers realized the behavior of their brain-like model and spiking neurons mimicked the processing patterns of artificial intelligence systems.

While observing the flow of information from one cobalt atom to another, across what scientists described as the world's smallest synthetic synapse, researchers noticed the neural network altered its behavior in response to the type of input it received.

"When stimulating the material over a longer period of time with a certain voltage, we were very surprised to see that the synapses actually changed," said lead researcher Alexander Khajetoorians, professor of scanning probe microscopy at Radboud. "The material adapted its reaction based on the external stimuli that it received. It learned by itself."

For now, scientists have only just glimpsed the technology's potential. In follow-up studies, researchers plan to scale up their network of information-processing cobalt atoms, and to perform tests to illuminate exactly how and why the network changes in response to new inputs.

"If we could eventually construct a real machine from this material, we would be able to build self-learning computing devices that are more energy efficient and smaller than today's computers," Khajetoorians said. "Yet, only when we understand how it works -- and that is still a mystery -- will we be able to tune its behavior and start developing it into a technology. It is a very exciting time."

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