New studies broaden the perspectives on what constitutes a “computer” and how small a computational unit can be.
When we define a “computer” as any device that processes information through input and output, it raises the questions of what objects can perform these computations and how small can these computers be. With transistors reaching the limits of miniaturization, finding answers to these questions becomes crucial, as they could lead to the development of a new computing paradigm.
In a new study published in the journal EPJ Plus by researchers from Tulane University in New Orleans, Louisiana, Gerard McCaul and his team demonstrate that atoms, one of the most basic building blocks of matter, can act as a reservoir for computing where all input-output processing is optical.
“We had the idea that the capacity for computation is a universal property that all physical systems share, but within that paradigm, there is a great profusion of frameworks for how one would go about actually trying to perform computations,” McCaul says.
He adds that one of the most important of these frameworks is neuromorphic or reservoir computing with a neuromorphic computer aiming to mimic the brain. This concept underpins the explosive development of machine learning and AI in the last few decades and leads to a potentially non-linear computer where output is not linearly proportional to the input. This is desirable as it could lead to a computing architecture flexible enough that any given output can be achieved, given a suitable input.
“That is, if we want some given computational result, we are guaranteed that some input to the computation exists that will achieve it,” McCaul says. “This is impossible if our system only exhibits a linear response!”
The team proposed a non-linear single-atom computer with the input information encoded directly into light and the output also in the form of light. The calculation is then determined by filters that the light