New material marks important step towards ‘quantum brain’

1 Feb 2021876 Views

Image: © Sergey Tarasov/Stock.adobe.com

Physicists have built an intelligent material that learns by physically changing itself, similar to how the human brain works.

Physicists from Radboud University in the Netherlands say they have made an important step towards what they are calling a “quantum brain”.

Currently, artificial intelligence works by recognising patterns in the world and learning new ones using machine learning software.

Bert Kappen, co-author of the research, said that while this has worked sufficiently until now, it is “a very energy-inefficient process”. The team therefore wanted to find out whether a piece of hardware could do the same, without the need of software.

‘We are at a state where we can start to relate fundamental physics to concepts in biology, like memory and learning’
– ALEXANDER KHAJETOORIANS

In findings published in Nature Nanotechnology, the researchers demonstrated that they can pattern and interconnect a network of cobalt atoms on black phosphorus.

Support Silicon Republic

This enabled them to build an intelligent material that stores and processes information, mimicking the autonomous behaviour of neurons and synapses in a human brain. Additionally, the researchers found that the material adapts itself.

Project leader and professor of scanning probe microscopy at Radboud University, Alexander Khajetoorians, said: “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. The material adapted its reaction based on the external stimuli that it received. It learned by itself,” he said.

The researchers hope to scale up the system and build a larger network of atoms as well as look into other potential quantum materials that can be used.

“We are at a state where we can start to relate fundamental physics to concepts in biology, like memory and learning,” said Khajetoorians.

“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 todays computers.”

Jenny Darmody is the deputy editor of Silicon Republic

editorial@siliconrepublic.com