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Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications.

Hari Hara Suthan Chittoor1, Osvaldo Simeone1

  • 1KCLIP Lab, Department of Engineering, King's College London, London WC2R 2LS, UK.

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Summary
This summary is machine-generated.

This study introduces Noise Aware-LOCCNet (NA-LOCCNet), a quantum machine learning approach for distributed quantum information processing over noisy channels. NA-LOCCNet enhances quantum entanglement distillation and state discrimination tasks, outperforming existing noiseless protocols.

Keywords:
distributed quantum computingentanglement distillationparameterized quantum circuitsquantum machine learningstate discrimination

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Area of Science:

  • Quantum Information Science
  • Quantum Machine Learning
  • Quantum Communication

Background:

  • Distributed quantum information processing relies on local operations and classical communications (LOCC).
  • Existing LOCC protocols often assume ideal, noiseless communication channels.
  • Real-world quantum communication channels are susceptible to noise, limiting protocol performance.

Purpose of the Study:

  • To develop a novel approach for designing LOCC protocols that account for noisy classical communication channels.
  • To enhance the performance of quantum entanglement distillation and quantum state discrimination tasks under realistic communication conditions.
  • To leverage quantum machine learning tools for optimizing LOCC protocols in the presence of noise.

Main Methods:

  • Utilizing quantum machine learning tools to design LOCC protocols.
  • Implementing local processing via parameterized quantum circuits (PQCs).
  • Optimizing PQCs to maximize average fidelity and success probability, explicitly accounting for communication errors.

Main Results:

  • The proposed Noise Aware-LOCCNet (NA-LOCCNet) approach effectively handles noisy classical communication channels.
  • NA-LOCCNet demonstrates significant advantages over protocols designed for noiseless communications.
  • The method successfully enhances quantum entanglement distillation and quantum state discrimination.

Conclusions:

  • Quantum machine learning offers a powerful framework for addressing noise in distributed quantum information processing.
  • NA-LOCCNet provides a robust and advantageous solution for LOCC protocols operating over noisy channels.
  • This work paves the way for more resilient and practical quantum communication systems.