‘Quantum Brain’ Would Mimic Our Own to Speed Up AI

Unless you’re in the lithium battery or paint business, you’re probably not familiar with cobalt. The result isn’t just a computer with the ability to learn. While the tech giants have massive data centers tailored to process computational needs, it’s inefficient and generates a huge carbon footprint. “Our new idea of building a ‘quantum brain’ based on the quantum properties of materials could be the basis for a future solution for applications in AI,” said lead author Dr. Alexander Khajetoorians at Radboud University in Nijmegen, the Netherlands.

The problem comes when these algorithms are run on current computers. You see, even state-of-the-art computers process information and store them in separate structures. The CPU or GPU, by itself, can’t store data. This means that data needs to be constantly shuttled between the processing and memory units.

It’s not a big deal for small things, like recognizing images, but for larger problems it rapidly slows the whole process down, while increasing energy use.

In other words, because AI mimics the brain, which has a completely alien structure to modern computers, there’s a fundamental incompatibility. While AI algorithms can be optimized for current computers, they’re likely to hit a dead end when it comes to efficiency. No data shuttling means less time and energy consumption, a win for AI and for the planet.

Another method uses synapses, which fine-tunes the degree a neuron can transmit the data and store them at the same time, using “states. ” Say you have a network of neurons, connected by synapses, that collectively store a chili recipe.

To tackle the problem of learning hardware, back in 2018 the team found that single cobalt atoms could potentially take over the role of neurons. At this atomic level, the mechanics of quantum physics also come into play, with some seriously intriguing results. In quantum mechanics, this weird “is the cat alive or dead” state is dubbed superposition. The team’s insight is that they could leverage these quantum properties to build a system similar to neurons and synapses in the brain.

To do so, they fabricated a system that overlays multiple cobalt atoms on top of a superconducting surface made of black phosphorus. They then tested whether they could induce firing and networking between the cobalt “neurons. Using tiny currents, the team fed the system simple binary data of 0s and 1s. Next, the team zapped the network of atoms with a small voltage change, similar to the input our neurons receive.

The tiny electrical zap generated behavior eerily similar to the brain’s mechanics. For example, it “double-tapped” the system, so that the quantum brain exhibited both processes analogous to neurons firing and changes in their synapses. Combining both inside a single material, cobalt, isn’t just novel. Similar to neurobiology, the system’s “synapses” also changed with time, based on the electrical input they experienced.

“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 Khajetoorians.

With the rise of quantum computing, algorithms tailored to the machine’s “spooky action at a distance” could further increase the system’s efficiency. Parallel processing, something our brains do very well but that stumps modern computers, has been scientists’ stretch goal for quantum computers since the 1990s. For their next pursuit, the team plans to uncover more quantum materials with different properties that may be more efficient than cobalt. And they’d like to dig into why the quantum brain works as well as it does.

“We are at a state where we can start to relate fundamental physics to concepts in biology, like memory and learning,” said Khajetoorians. Despite the unknowns, the study opens up an exciting field at the nexus between neuroscience, quantum computing, and AI. “It is a very exciting time,” said Khajetoorians.