Telemetry-Based Server Selection in the Quantum Internet via Cross-Layer Runtime Estimation

2026-02-24

AI Summary

This work addresses the challenge of efficiently selecting optimal remote servers in quantum networks, where end-to-end execution time is tightly coupled with server processing, classical feedforward latency, and entanglement distribution protocols. To overcome this, the authors propose $T_{\text{max}}$, a lightweight runtime scoring metric that aggregates coarse-grained telemetry across protocol layers to enable online server selection without requiring calibrated weighting parameters. This approach represents the first use of simple cross-layer telemetry for calibration-free decision-making and further offers actionable guidance for resource planning by identifying performance bottlenecks. Evaluated through NetSquid simulations using parameter-agnostic VQE workloads, Sobol global sensitivity analysis, and multi-user contention models, $T_{\text{max}}$ achieves normalized average regret below 10% across diverse scenarios and demonstrates robust performance under classical communication jitter for multi-shot tasks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

The Quantum Internet will allow clients to delegate quantum workloads to remote servers over heterogeneous networks, but choosing the server that minimizes end-to-end execution time is difficult because server processing, feedforward classical communication, and entanglement distribution can overlap in protocol-dependent ways and shift the runtime bottleneck. We propose $T_{\max}$, a lightweight runtime score that sums coarse telemetry from multiple layers to obtain a conservative ranking for online server selection without calibrating weights for each deployment. Using NetSquid discrete-event simulations of a modified parameter-blind VQE (PB-VQE) workload, we evaluate $T_{\max}$ on pools of 10,000 heterogeneous candidates (selecting among up to 100 per decision) across crossover and bottleneck-dominated regimes, including temporal jitter scenarios and jobs with multiple shots. $T_{\max}$ achieves single-digit mean regret normalized by the oracle (below 10%) in both regimes and remains in the single-digit range under classical communication latency jitter for multi-shot jobs, while performance degrades for single-shot jobs under severe jitter. To connect performance to deployment planning, we derive an operating map based on requirements relating distance and entanglement rate requirements to protocol level counts, quantify how simple multiuser contention shifts the crossover, and use Sobol global sensitivity analysis to identify regime-dependent bottlenecks. These findings suggest that simple cross-layer telemetry can enable practical server selection while providing actionable provisioning guidance for emerging Quantum Internet services.

Problem

• Quantum Internet
• server selection
• runtime bottleneck
• entanglement distribution
• telemetry

Innovation

• Telemetry-based server selection
• Cross-layer runtime estimation
• Quantum Internet
• Lightweight scoring metric
• Global sensitivity analysis