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

Abstract

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
JournalEntropy
Volume24
Issue number6
DOIs
StatePublished - Jun 2022

Keywords

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

Fingerprint

Dive into the research topics of 'Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance'. Together they form a unique fingerprint.

Cite this