The realization of distributed quantum neural networks (DQNNs) over quantum internet infrastructures faces fundamental challenges arising from the fragile nature of entanglement and the demanding synchronization requirements of distributed learning. We introduce a Consensus-Entanglement-Aware Scheduling (CEAS) framework that co-designs quantum consensus protocols with adaptive entanglement management to enable robust synchronous training across distributed quantum processors. CEAS integrates fidelity-weighted aggregation, in which parameter updates are weighted by quantum Fisher information to suppress noisy contributions, with decoherence-aware entanglement scheduling that treats Bell pairs as perishable resources subject to exponential decay. The framework incorporates quantum-authenticated Byzantine fault tolerance, ensuring security against malicious nodes while maintaining compatibility with noisy intermediate-scale quantum (NISQ) constraints. Our theoretical analysis establishes convergence guarantees under heterogeneous noise conditions, while numerical simulations demonstrate that CEAS maintains 10-15 percentage points higher accuracy compared to entanglement-oblivious baselines under coordinated Byzantine attacks, achieving 90 percent Bell-pair utilization despite coherence time limitations. This work provides a foundational architecture for scalable distributed quantum machine learning, bridging quantum networking, distributed optimization, and early fault-tolerant quantum computation.

翻译：在量子互联网基础设施上实现分布式量子神经网络面临根本性挑战，这些挑战源于纠缠的脆弱性和分布式学习严格的同步要求。本文提出一种共识-纠缠感知调度框架，该框架通过协同设计量子共识协议与自适应纠缠管理机制，实现跨分布式量子处理器的鲁棒同步训练。该框架集成了保真度加权聚合（其中参数更新由量子费希尔信息加权以抑制噪声贡献）和退相干感知纠缠调度（将贝尔态视为服从指数衰减的易损资源）。该框架融合了量子认证的拜占庭容错机制，在确保抵御恶意节点安全性的同时，保持与噪声中等规模量子约束的兼容性。我们的理论分析建立了异构噪声条件下的收敛保证，而数值模拟表明，在协同拜占庭攻击下，相比忽略纠缠的基线方法，该框架能保持10-15个百分点的精度优势，并在相干时间限制下实现90%的贝尔态利用率。本工作为可扩展的分布式量子机器学习提供了基础架构，搭建了量子网络、分布式优化与早期容错量子计算之间的桥梁。