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Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance.

Angeles Alejandra Sánchez-Manilla1, Itzamá López-Yáñez2, Guo-Hua Sun1

  • 1Centro de Investigación en Computación, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Juan de Dios Bátiz s/n esq. Miguel Othón de Mendizábal, Mexico City 07700, Mexico.

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Summary
This summary is machine-generated.

This study introduces a quantum associative memory using Hamming distance for pattern recovery. It leverages quantum superposition and dimensionality reduction for enhanced memory performance.

Keywords:
Alpha-Beta associative modelhamming distancepattern recognitionquantum associative memoryquantum machine learning

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Area of Science:

  • Quantum Computing
  • Information Retrieval
  • Memory Systems

Background:

  • Associative memory models are crucial for information retrieval.
  • Quantum computing offers novel approaches to enhance computational tasks.
  • Dimensionality reduction techniques are vital for managing large datasets.

Purpose of the Study:

  • To present a novel quantum associative memory, Alpha-Beta HQAM.
  • To integrate Hamming distance calculation within a quantum framework for pattern recovery.
  • To explore the benefits of storing patterns in quantum superposition.

Main Methods:

  • Developed the Alpha-Beta HQAM model combining classical associative memory principles with quantum computation.
  • Implemented a quantum subroutine for efficient Hamming distance computation during pattern retrieval.
  • Utilized quantum superposition for initial pattern storage.
  • Conducted experiments using IBM's Qiskit library to validate performance.

Main Results:

  • Demonstrated the viability of the proposed quantum associative memory.
  • Showcased the effectiveness of using Hamming distance for pattern recovery in a quantum context.
  • Highlighted the performance advantages gained from employing quantum superposition.

Conclusions:

  • The Alpha-Beta HQAM presents a promising quantum approach to associative memory.
  • Quantum computation can significantly enhance pattern recovery mechanisms.
  • Further research can explore scalability and applications in complex data retrieval.