Phase transitions in quantum pattern recognition.

With the help of quantum mechanics one can formulate a model of associative memory with optimal storage capacity. This model is generalized by introducing a parameter playing the role of an effective temperature. The corresponding thermodynamics provides criteria to tune the efficiency of quantum pattern recognition. It is shown that the associative memory undergoes a phase transition from a disordered, high-temperature phase with no correlation between input and output to an ordered, low-temperature phase with minimal input-output Hamming distance.