**Deanna Csomo McCool | December 12, 2018 **

Your computer performs most tasks well. For word processing, certain computations, graphic arts, and web surfing, the digital box on your desk is the best tool for the job. But the way your computer works, 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 like Zoltán Toroczkai, professor in the Department of Physics and concurrent professor in the Department of Computer Science and Engineering, are interested in reviving analog computing at a time when digital computing has reached its maximum potential.

Toroczkai and collaborators have been working toward developing a novel mathematical approach that will help advance computation beyond the digital framework. His most recent paper, published in Nature Communications, describes a new mathematical, analog “solver” that can potentially find the best solution to NP-hard problems. NP-hardness is a theory of computational complexity, with problems that are famous for their difficulty. And when the number of variables is large, problems associated with scheduling, protein folding, bioinformatics, medical imaging, and many other areas are nearly unsolvable with known methods. After testing their new method on a variety of NP-hard problems, the researchers concluded their solver has the potential to lead to better, and possibly faster, solutions than can be computed digitally.

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. They first used gears and vacuum tubes, and later, transistors, that could be configured to solve problems with a range of variables. They perform mathematical functions directly. For instance, to add 5 and 9, analog computers add voltages that correspond to those numbers, and then instantly obtain the correct answer. 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. Computer algorithms, in the form of software, are sets of instructions that tell the computer hardware how to perform. Because the process is restricted to the use of 0s and 1s, this also makes their programming simpler, and allowed digital computing to dominate for nearly 70 years.

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