利用共享随机性增强纠缠纯化
Enhancing Entanglement Purification with Shared Randomness
浏览论文内容
中文总结 AI 辅助
研究在源标签不可用时如何增强纠缠纯化，利用经典共享随机性与缓冲存储器，积累多轮纠缠分发并洗牌存储的纠缠态作为输入，证明对任意\(n\)个Werner源及固定协议，该方法能提高成功概率和保真度且随积累轮次递增。
中文摘要 AI 辅助
纠缠纯化协议对于提高纠缠保真度以支持容错分布式量子信息处理至关重要。实际的纠缠源通常是异构的，并且在纠缠纯化协议层可能无法获得源标签。我们表明，当源标签不可用时，经典共享随机性与缓冲存储器一起，可以在无需状态表征或纠缠纯化协议电路优化的情况下增强纠缠纯化。该策略是积累多个纠缠分发轮次，然后使用共享随机性对所有存储的纠缠态进行洗牌，再将它们打包作为纠缠纯化协议的输入。对于任何\(n\)个 Werner 源和任何固定的\(n\)对\(1\)双局域 Clifford 纠缠纯化协议，我们证明，对于每个\(n\)、每个有限的积累轮次数量以及在渐近极限情况下，积累和洗牌相比于不积累和洗牌的基线提高了预期成功概率和成功加权输出贝尔保真度，并且这种改进随着积累轮次数量单调增加。
英文摘要
Entanglement purification protocols (EPPs) are essential for improving entanglement fidelity to support fault-tolerant distributed quantum information processing. Practical entanglement sources are often heterogeneous and source labels may be unavailable at the EPP layer. We show that classical shared randomness, together with buffer memories, can enhance entanglement purification when source labels are unavailable, without state characterization or EPP circuit optimization. The strategy is to accumulate multiple entanglement distribution rounds and then use shared randomness to shuffle all the stored entangled states before packaging them as inputs to the EPP. For any $n$ Werner sources and any fixed $n$-to-1 bilocal Clifford EPP, we prove that accumulating and shuffling improves the expected success probability and the success-weighted output Bell fidelity over the baseline without accumulating and shuffling, for every $n$, for every finite number of accumulation rounds and in the asymptotic limit, and the improvement increases monotonically with the number of accumulation rounds.
