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Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning

Jun-Cheol Jeon1, Amjad Almatrood2, Hyun-Il Kim1

  • 1Department of Convergence Science, Kongju National University, Gongju 32588, Republic of Korea.

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This study introduces a quantum associative memory cell using quantum-dot cellular automata for efficient AI data learning. The novel design enhances data retrieval and reduces quantum cost and energy dissipation.

Keywords:
artificial intelligent learningcontent addressable memorylow-power QCA circuitsnanotechnologyquantum computingquantum-dot cellular automata

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Area of Science:

  • Quantum computing
  • Artificial intelligence
  • Nanotechnology

Background:

  • Effective data learning in AI requires content-based retrieval, not just memory addresses.
  • Content-Addressable Memory (CAM) offers efficient data retrieval.
  • Quantum computing environments necessitate novel CAM designs.

Purpose of the Study:

  • To propose a quantum structure for an associative memory cell for effective AI data learning.
  • To design a quantum-dot cellular automata (QCA)-based Content-Addressable Memory (CAM) cell.
  • To enhance data retrieval efficiency and reduce energy dissipation in quantum memory systems.

Main Methods:

  • Designed a multilayer XNOR gate based on electron interactions.
  • Developed a QCA-based CAM cell incorporating the novel XNOR gate.
  • Verified area and time efficiency using QCADesigner simulations.
  • Analyzed energy dissipation using QCADesigner-E simulations.

Main Results:

  • Achieved significant reductions in quantum cost for XOR gates (≥70%) and CAM cells (≥15%) compared to existing research.
  • Demonstrated physical potential energy due to electron interactions within the QCA cell.
  • Proposed an additional CAM circuit reducing energy dissipation by over 27%.

Conclusions:

  • The proposed QCA-based CAM cell offers superior area and time efficiency for quantum associative memory.
  • The novel design significantly reduces quantum cost and energy dissipation, outperforming current state-of-the-art.
  • This work advances quantum computing architectures for efficient AI data processing.