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Hopfield networks are a variant of associative memory that recall informationstored in the couplings of an Ising model. Stored memories are fixed points forthe network dynamics that correspond to energetic minima of the spin state. Weformulate the recall of memories stored in a Hopfield network using energyminimization by adiabatic quantum optimization (AQO). Numerical simulations ofthe quantum dynamics allow us to quantify the AQO recall accuracy with respectto the number of stored memories and the noise in the input key. We alsoinvestigate AQO performance with respect to how memories are stored in theIsing model using different learning rules. Our results indicate that AQOperformance varies strongly with learning rule due to the changes in the energylandscape. Consequently, learning rules offer indirect methods forinvestigating change to the computational complexity of the recall task and thecomputational efficiency of AQO.

Adiabatic Quantum Optimization for Associative Memory Recall