ABSTRACT
Quantum associative memory (QuAM) in the domain of quantum computation is a model with a capacity exponential in the number of neurons. Most quantum machine learning algorithms including quantum associative memory suffer from the input destruction problem where the classical data must be read and after the measurement the superposition collapses. Most quantum machine learning algorithms including quantum associative memory suffer from the input destruction problem. The efficient preparation of data is possible in part for spares data. However, the input destruction problem is not solved till today, and usually theoretical speed ups are analyzed by ignoring the input problem, which is the main bottleneck for data encoding. The naming of the phases is in analogy to a living organism that prepares itself during the sleep for an active day. The advantage of quantum approach is present in the active phase.
