  With the help of quantum mechanics one can formulate a model of associative
memory with optimal storage capacity. I generalize this model by introducing a
parameter playing the role of an effective temperature. The corresponding
thermodynamics provides criteria to tune the efficiency of quantum pattern
recognition. I show 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.
