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 language | English |
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Article number | 789 |
Journal | Entropy |
Volume | 24 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2022 |
Keywords
- Alpha-Beta associative model
- hamming distance
- pattern recognition
- quantum associative memory
- quantum machine learning