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Method for Quantum Denoisers Using Convolutional Neural Network.

Bong-Hyun Kim1, S Madhavi2

  • 1School of Software, Computer Engineering Major, Seowon University 377-3, Musimseo-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do 28674, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel quantum denoiser using a convolutional neural network to reduce noise in quantum channels. The model successfully denoises Greenberger-Horne-Zeilinger states, enhancing quantum communication fidelity.

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Area of Science:

  • Quantum Information Science
  • Quantum Communication

Background:

  • High-dimensional entanglement is crucial for quantum information science applications like quantum teleportation.
  • Distribution of entangled pairs (EPR) can lead to decreased fidelity due to channel noise.
  • Existing methods for noise reduction in quantum channels often do not utilize machine learning.

Purpose of the Study:

  • To propose and evaluate a novel quantum denoiser for mitigating noise in quantum channels.
  • To enhance the fidelity and quantification of entanglement in quantum states, specifically GHZ states.
  • To introduce a machine learning-based approach for noise reduction in quantum communication.

Main Methods:

  • Development of a quantum denoiser (CNQD) utilizing a feedforward convolutional neural network.
  • Generation of a random noise source to simulate channel imperfections.
  • Training and tuning the neural network model with entangled GHZ states subjected to various noise types, including spin flips and bit flips.

Main Results:

  • The proposed CNQD model successfully denoises Greenberger-Horne-Zeilinger (GHZ) states corrupted by spin and bit flip errors.
  • The model effectively filters irrelevant noise information without compromising the encoded quantum states.
  • Demonstrated successful denoising of quantum states with varying phases, including zero and those between [0, ∏].

Conclusions:

  • The developed convolutional neural network-based quantum denoiser offers an effective solution for noise reduction in quantum channels.
  • The CNQD model facilitates optimal quantum communication by preserving the integrity of entangled states like GHZ states.
  • This work highlights the potential of machine learning, specifically neural networks, for advancing noise mitigation strategies in quantum information science.