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

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.