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

TL;DR

This paper introduces Noise Aware-LOCCNet, a quantum machine learning approach that optimizes distributed quantum protocols over noisy classical channels, improving performance in entanglement distillation and state discrimination.

Contribution

It proposes a novel quantum machine learning framework for designing LOCC protocols that are robust to communication noise, advancing practical distributed quantum information processing.

Findings

NA-LOCCNet outperforms existing noiseless protocols under noisy conditions

Optimized PQCs improve fidelity and success probability in quantum tasks

The approach demonstrates robustness to classical communication errors

Abstract

Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which classical communication takes place over noisy channels, and we propose to address the design of LOCC protocols in this setting via the use of quantum machine learning tools. We specifically focus on the important tasks of quantum entanglement distillation and quantum state discrimination, and implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity and average success probability in the respective tasks, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.