Algorithmic complexity and the principles of molecular computing

The human brain is constantly generating very good solutions to highly difficult physiological and cognitive problems. In any computational system, these problems take the form 

Optimality or near-optimality in computational performance is the result of error feedback leading to modification of output [15]. The process is described by an alphabet Q, I, Z, d, w where Qj is the structure s internal states, I represents the environmental inputs, Z is its output values, d is a next-state mapping function such that d I X Q, a Qj and w is an output function such that w Ij X Qj a Z,. Error E may then be expressed as the sum of accumulating environmental mispredictions 

One of the most unusual features of QM is that measurements of the position, momentum, spin, energy, or some other physical observable of the same type of particle under identical experimental conditions produce different experimental readings this is known as the Measurement Problem . Repetitions of the experiment permit the collecting of a set of probabilities C where 

The relationship between linear superposition and quantum computing may be illustrated by electron spin which may be thought of as a binary system ( spin-up and spin-down ). Unlike informational tokens in classical digital computers which are either 0 or 1, both spin states coexist in the same state-space (i.e., are superposed) prior to an experimental measurement. Due to this situation (which cannot be visualized) 1 electron and 4 nearest neighbors in a weakly coupled system would exist in 2 or 32 configurations at one step of a computation. A biomolecular structure (e.g., a neural membrane) utilizing such parallelism could rapidly compute solutions to combinatorially explosive problems of physiology and behavior [31-33]. 

Transduction and amplification require macroscopic-microscopic informational state-space mapping. 

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