AI & ML
Rate-Fidelity Control for Wide-Area Quantum Links
arXiv cs.NI0 views
arXiv:2608.07163v2 Announce Type: replace-cross Abstract: Quantum network links must distribute entanglement at high rates while satisfying application-specified fidelity demands. However, wide-area deployed fiber links suffer from polarization drift which destabilizes end-to-end fidelity and forces periodic compensation. Current deployments often use active stabilization with fixed control policies, and improvements generally stem from advances in quantum hardware. Meanwhile, software control remains relatively underexplored. Here, we formulate quantum link operation as a joint control problem over tunable rate-fidelity tradeoffs and uncontrollable link drift. From this framework, we construct a link control protocol that dynamically adapts source pump power and polarization compensation to maximize entanglement distribution rate subject to a minimum fidelity constraint. We evaluate the protocol through trace-driven simulations driven by data from a 64 km deployed optical fiber. Compared with optimized static policies, our adaptive controller improves mean entanglement distribution rate by 14% over a 24 hour trace, without requiring any offline policy optimization. Our results show that software-based physical layer control can provide a practical mechanism for improving near-term quantum link performance without requiring additional quantum hardware.
Source:https://arxiv.org/abs/2608.07163PreviousWho Needs DRAM? We Have FiberNextWhiel: A Neurosymbolic Platform for Certified Relational Program Verification
Related posts
Meta Quest Promo Codes and Coupons for November 2026
0Wired
BullMQ in NestJS: Retries, Idempotency and Dead-Letter Queues
0dev.to
80.6% at Chores, 12% at Real Work: AI's Benchmark Week
0dev.to
Efficient estimation of regression models for recurrent events in presence of terminal events
0arXiv stat.CO
Local Whittle estimation for distributional time series: A regularization framework in Bayes Hilbert space
0arXiv stat.CO
Inventory and Assortment Optimization under General Choice: A Unified Approximation Framework
0arXiv stat.CO