An Adaptive Purification Controller for Quantum Networks: Dynamic Protocol Selection and Multipartite Distillation

2026-01-26

AI Summary

This work addresses the challenge in dynamic quantum networks where static entanglement purification strategies struggle to simultaneously achieve high fidelity and high distribution rates, often suffering from a “fidelity cliff.” To overcome this, the authors propose an Adaptive Purification Controller (APC) that formulates protocol selection as a resource allocation problem. By integrating dynamic programming with Pareto pruning, APC dynamically switches among purification protocols—such as BBPSSW and DEJMPS—in real time to maximize the delivery rate of entangled pairs meeting a target fidelity threshold. This approach represents the first adaptive selection framework for purification protocols tailored to dynamic noise environments and is extensible to multipartite GHZ states and continuous-variable systems. Simulations demonstrate that APC significantly reduces resource waste under high noise, achieves millisecond-level decision latency, and enhances effective throughput while maintaining high fidelity.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Privacy-enhancing technologies

Abstract

Efficient entanglement distribution is a cornerstone of the Quantum Internet. However, physical link parameters such as photon loss, memory coherence time, and gate error rates fluctuate dynamically, rendering static purification strategies suboptimal. In this paper, we propose an Adaptive Purification Controller (APC) that automatically optimizes the entanglement distillation sequence to maximize the goodput, i.e., the rate of delivered pairs meeting a strict fidelity threshold. By treating protocol selection as a resource allocation problem, the APC dynamically switches between purification depths and protocols (BBPSSW vs. DEJMPS) to navigate the trade-off between generation rate and state quality. Using a dynamic programming planner with Pareto pruning, simulation results show that our approach mitigates the"fidelity cliffs"inherent in static protocols and reduces resource wastage in high-noise regimes. Furthermore, we extend the controller to heterogeneous scenarios, and evaluate it for both multipartite GHZ state generation and continuous-variable systems using effective noiseless linear amplification models. We benchmark its computational overhead, showing decision latencies in the millisecond range per link in our implementation.

Problem

• entanglement distillation
• quantum networks
• dynamic link parameters
• fidelity threshold
• resource allocation

Innovation

• Adaptive Purification Controller
• entanglement distillation
• dynamic protocol selection
• multipartite GHZ states
• quantum network