LAUSR

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights ($$\eta $$η) should equal the numbers of unknown bits in the input pattern (d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter $$\alpha $$α.