Suppose you want to build a special machine, one that can plot a traveling salesperson’s route or schedule all the flights at an international airport. That is, the sorts of problems that are incredibly complex, with a staggering number of variables. For now, the best way to crunch the numbers for these optimization problems remains a powerful computer.
But research into developing analog optimizers—machines that manipulate physical components to determine the optimized solution—is providing insight into what is required to make them competitive with traditional computers.
To that end, a paper published today in Science Advances provides the first experimental evidence that high connectivity, or the ability for each physical component to directly interact with the others, is a vital component for these novel optimization machines. “Connectivity is very, very important, it’s not something one should ignore,” says Peter McMahon, a postdoctoral researcher at Stanford, who participated in the research.