And now, news from Germany.
A new parallel-computing approach can solve combinatorial problems, according to a study published in Proceedings of the National Academy of Sciences. Researchers from the Max Planck Institute of Molecular Cell Biology and Genetics and the Dresden University of Technology collaborated with an international team on the technology. The researchers note significant advances have been made in conventional electronic computers in the past decades, but their sequential nature prevents them from solving problems of a combinatorial nature. The number of calculations required to solve such problems grows with the size of the problem, making them intractable for sequential computing. The new approach addresses these issues by combining well-established nanofabrication technology with molecular motors that are very energy-efficient and inherently work in parallel. The researchers demonstrated the parallel-computing approach on a benchmark combinatorial problem that is very difficult to solve with sequential computers. The team says the approach is scalable, error-tolerant, and dramatically improves the time to solve combinatorial problems of size N. The problem to be solved is "encoded" within a network of nanoscale channels by both mathematically designing a geometrical network that is capable of representing the problem, and by fabricating a physical network based on this design using lithography. The network is then explored in parallel by many protein filaments self-propelled by a molecular layer of motor proteins covering the bottom of the channels.