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A quantum Hopfield associative memory implemented on an actual quantum processor.

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We developed a Quantum Hopfield Associative Memory (QHAM) using a novel quantum neuron. This QHAM functions on current quantum hardware, showing resilience to noise and potential for machine learning applications.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Quantum Hopfield Associative Memory (QHAM) is a novel architecture for associative memory.
  • Quantum computing offers potential advantages for machine learning tasks.
  • Current NISQ-era hardware presents challenges for complex quantum algorithms.

Purpose of the Study:

  • To present a Quantum Hopfield Associative Memory (QHAM) design.
  • To demonstrate the QHAM's functionality on real quantum hardware.
  • To analyze the QHAM's performance and resilience to noise.

Main Methods:

  • Developed a quantum neuron design for QHAM implementation.
  • Simulated QHAM performance using hardware noise models.
  • Implemented and tested the QHAM on the IBM Quantum Experience (ibmq_16_melbourne).

Main Results:

  • The QHAM demonstrated functional capabilities on NISQ-era quantum hardware.
  • The quantum neuron and QHAM showed resilience to hardware noise.
  • Low qubit overhead and gate complexity were observed.

Conclusions:

  • The developed QHAM is a significant advancement for machine learning on quantum computers.
  • The QHAM's noise resilience and low resource requirements make it suitable for NISQ devices.
  • This work paves the way for practical quantum associative memory systems.