Consensus Protocols for Entanglement-Aware Scheduling in Distributed Quantum Neural Networks

TL;DR

This paper introduces CEAS, a framework for robust, entanglement-aware synchronization in distributed quantum neural networks, improving accuracy and resource utilization under noisy and adversarial conditions.

Contribution

It presents a novel co-designed protocol integrating quantum consensus, adaptive entanglement management, and fault tolerance for scalable distributed quantum learning.

Findings

CEAS achieves 10-15% higher accuracy than baselines under Byzantine attacks.

Maintains 90% Bell-pair utilization despite decoherence.

Provides convergence guarantees under heterogeneous noise.

Abstract

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.