Noise based logic: Why noise?

Noise-based logic, similarly to the brain, is using different random noises to represent the different logic states. While the operation of brain logic is still an unsolved problem, noise-based logic shows potential advantages of reduced power dissipation and the ability of large parallel operations with low hardware and time complexity. But there is a fundamental question: is randomness really needed out of orthogonality? Orthogonal signal systems (similarly to orthogonal noises) can also represent multidimensional logic spaces and superpositions. So, does randomness add any advantage to orthogonality or is it disadvantageous due to the required statistical evaluation of signals? In this talk, after some general physical considerations, we show and analyze some specific examples to compare the computational complexities of logic systems based on orthogonal noise and sinusoidal signal systems, respectively. The conclusion is that, in certain special-purpose applications that are particularly relevant for mimicking quantum informatics, noise-based logic is exponentially better than its sinusoidal version: its computational complexity (time and hardware) can exponentially be smaller to perform the same task.