The processing power of standard computers is expected to reach its maximum in the next 10 to 25 years. But even at thismaximum power, computers in their current form won’t be able to handle certain tasks, namely being able to combine variables to come up with multiple answers and find the best solution.
Now, an entirely new type of computer that blends optical and electrical processing could solve this issue and it could even save costs.
“This is a machine that’s in a sense the first in its class, and the idea is that it opens up a sub-field of research in the area of non-traditional computing machines,” said Peter McMahon, co-author of the paper published in the journal Science. “There are many, many questions that this development raises and we expect that over the next few years, several groups are going to be investigating this class of machine and looking into how this approach will pan out.”
The type of problem the computer has been designed to solve is officially known as a “combinatorial optimisation problem” but is more commonly known by an example called the Travelling Salesman problem.
In the real world, this type of problem is used for finding the best path for delivery trucks, reducing interference in wireless networks, and determining how proteins fold. The researchers said that even small improvements in these areas could save huge amounts of money.
How does the Ising machine work?
The Stanford team has built what’s called an Ising machine, named after the mathematical model of magnetism. The machine acts like a “reprogrammable network of artificial magnets” where each magnet only points up or down and, like a real magnetic system, it tends to operate at low energy.
The theory is that, if the connections among a network of magnetscan be programmed to represent the problem at hand, once they settle on the best, low-energy options, the solution can be taken from the magnets’ final state. In the case of the travelling salesman, for example, each artificial magnet in the Ising machine represents the position of a city in a particular path.
Rather than using magnets on a grid, the Stanford team used a laser system, known as a degenerate optical parametric oscillator, that, when turned on, represents either an upward- or downward-pointing “spin.” The city’s position on a path is represented by pulses of the laser.
The so-called “pulse-to-pulse couplings” represent the programming of the problem. Then, the machine is turned on to try to find a solution, which is found by measuring the final output phases of the pulses.
The latest Stanford Ising machine builds on one built two years ago and is more affordable and practical version than the original, made possible by replacing the controllable optical delays with a digital electronic circuit. The circuit emulates the optical connections among the pulses in order to program the problem, while the laser system still solves it.
Furthermore, nearly all of the materials used to make this machine are off-the-shelf elements already used for telecommunications. That, coupled with how simple the programming is, makes it easy to scale up, explained the researchers. At the moment, Stanford’s Ising machine can solve 100-variable problems with any arbitrary set of connections between variables, and it has been tested on thousands of scenarios.
It can’t quite beat the processing power of traditional digital computers when it comes to combinatorial optimisation, but the researchers said “it is gaining ground fast.”
“I think it’s an exciting avenue of exploration for finding alternative computers. It can get us closer to more efficient ways of tackling some of the most daunting computational problems we have,” said Marandi. “So far, we’ve made a laser-based computer that can target some of these problems, and we have already shown some promising results.”