Your computer performs most tasks well, but, with its style of mathematics that relies on the binary code system of “on” and “off” 1s and 0s, isn’t ideal for solving every problem.
That’s why researchers are interested in reviving analog computing at a time when digital computing has reached its maximum potential.
Zoltán Toroczkai, professor in the Department of Physics and concurrent professor in the Department of Computer Science and Engineering at the University of Notre Dame, and collaborators have been working toward developing a novel mathematical approach that can potentially find the best solution to NP-hard problems.
Analog computers were used to predict tides from the early to mid-20th century, guide weapons on battleships and launch NASA’s first rockets into space, among other uses. However, analog computers were cumbersome and prone to “noise” — disturbances in the signals — and were difficult to re-configure to solve different problems, so they fell out of favor.
Digital computers emerged after transistors and integrated circuits were reliably mass produced, and for many tasks they are accurate and sufficiently flexible.
A challenge for analog computing rests with the design of continuous algorithms. Unlike digital computing, which has a long history in algorithm development, algorithms for analog computers lack a similar knowledge base and thus are very difficult to design.
The next step is to design and build devices that would be built for specific tasks, and not for everyday computing needs. However, there are engineering problems that need to be solved at this point, such as spurious capacities and better noise control, but it’s going to get there.