  I review and expand the model of quantum associative memory that I have
recently proposed. In this model binary patterns of n bits are stored in the
quantum superposition of the appropriate subset of the computational basis of n
qbits. Information can be retrieved by performing an input-dependent rotation
of the memory quantum state within this subset and measuring the resulting
state. The amplitudes of this rotated memory state are peaked on those stored
patterns which are closest in Hamming distance to the input, resulting in a
high probability of measuring a memory pattern very similar to it. The accuracy
of pattern recall can be tuned by adjusting a parameter playing the role of an
effective temperature. This model solves the well-known capacity shortage
problem of classical associative memories, providing an exponential improvement
in capacity. The price to pay is the probabilistic nature of information
retrieval, a feature that, however, this model shares with our own brain.
