Non-unitary Quantum Associative Memory

Quantum associative memories (QAM) have storage capacity exponentially larger than classical associative memories. Given an input with n bits, the QAM can load all of 2n binary patterns. In this paper we propose a nonunitary quantum memory retrieval algorithm. The proposed method is used to recover incomplete input information and also in pattern classification task as an ensemble of memories. The advantages of the proposed method are the simplicity of recovery procedure in a unique iteration and the fact the circuit has linear complexity in function of the input length with deterministic output.