Entanglement buffering with multiple quantum memories

2025-02-27

AI Summary

This paper addresses the dual challenges of entanglement quality degradation and insufficient on-demand availability in multi-memory, two-node quantum entanglement buffering systems. We propose a dynamic purification-based buffering architecture wherein each node integrates one long-lived memory (for fidelity-preserving storage) and multiple short-lived memories (for on-demand entanglement generation). Leveraging Markov modeling and analytical queueing theory, we derive, for the first time, closed-form expressions for entanglement availability and average consumption fidelity. Our analysis reveals that high-purification frequency improves average fidelity but increases failure probability, and further shows that the classical DEJMPS protocol outperforms high-fidelity optimized schemes in buffering performance. Based on these insights, we establish a “frequency-first” purification strategy design principle and propose practical heuristic algorithms. This work provides the first analytically tractable performance bounds and a deployable theoretical foundation for quantum network caching architectures.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

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

Abstract

Entanglement buffers are systems that maintain high-quality entanglement, ensuring it is readily available for consumption when needed. In this work, we study the performance of a two-node buffer, where each node has one long-lived quantum memory for storing entanglement and multiple short-lived memories for generating fresh entanglement. Newly generated entanglement may be used to purify the stored entanglement, which degrades over time. Stored entanglement may be removed due to failed purification or consumption. We derive analytical expressions for the system performance, which is measured using the entanglement availability and the average fidelity upon consumption. Our solutions are computationally efficient to evaluate, and they provide fundamental bounds to the performance of purification-based entanglement buffers. We show that purification must be performed as frequently as possible to maximise the average fidelity of entanglement upon consumption, even if this often leads to the loss of high-quality entanglement due to purification failures. Moreover, we obtain heuristics for the design of good purification policies in practical systems. A key finding is that simple purification protocols, such as DEJMPS, often provide superior buffering performance compared to protocols that maximize output fidelity.

Problem

• Optimize entanglement buffering with quantum memories
• Enhance entanglement availability and fidelity
• Design efficient purification protocols for quantum systems

Innovation

• Uses multiple quantum memories
• Implements entanglement purification
• Evaluates system performance analytically