Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance

Angeles Alejandra Sánchez-Manilla, Itzamá López-Yáñez, Guo Hua Sun

Research output: Contribution to journalArticlepeer-review


This work presents a quantum associative memory (Alpha-Beta HQAM) that uses the Hamming distance for pattern recovery. The proposal combines the Alpha-Beta associative memory, which reduces the dimensionality of patterns, with a quantum subroutine to calculate the Hamming distance in the recovery phase. Furthermore, patterns are initially stored in the memory as a quantum superposition in order to take advantage of its properties. Experiments testing the memory’s viability and performance were implemented using IBM’s Qiskit library.

Original languageEnglish
Article number789
Issue number6
StatePublished - Jun 2022


  • Alpha-Beta associative model
  • hamming distance
  • pattern recognition
  • quantum associative memory
  • quantum machine learning


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