Parallel computation with molecular-motor-propelled agents in nanofabricated networks
Parallel computation with molecular-motor-propelled agents in nanofabricated networks is a paper by Dan Nicolau Jr. et. al. in the Proceedings of the National Academy of Sciences (PNAS).
Abstract
The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers.
There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation.
However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective.
Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device.
Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem.
This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation.
We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem.
Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.
