无需半定规划的量子密钥分发密钥率高效计算
Efficient Computation of QKD Key Rates without Semidefinite Programming
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中文总结 AI 辅助
本研究提出一种仅需特征值计算的QKD密钥率高效计算方法，将内存需求从d⁴降至d²，在树莓派上实现实时估计，性能远超现有工作站基准，为QKD硬件嵌入安全分析开辟路径。
中文摘要 AI 辅助
将观测数据转化为可靠的安全密钥率估计值是量子密钥分发（QKD）设备运行的关键步骤。我们为此任务提供了一种仅需特征值计算的计算方法，因此兼具快速性与资源高效性。相比之下，现有方法依赖半定规划或熵锥上的规划，其内存需求随底层希尔伯特空间维度 d 的四次方（d⁴）缩放。我们的方法将该需求降低至 d²。我们算法的最简实现仅需不到100行Common Lisp代码。我们在具备1GB内存和Cortex-A53处理器的树莓派上演示了实时密钥率估计。尽管使用这些有限资源，我们的实现仍比现有基于工作站的基准测试快数个数量级。使用有理近似可几乎无额外开销地纳入非数值验证。这些结果为将完整的数值安全分析直接嵌入QKD硬件开辟了道路。
英文摘要
Translating observed data into a reliable estimate of the secure key rate is a crucial step for operating a quantum key distribution device. We provide a computational method for this task that only requires eigenvalue computations and is therefore both fast and resource efficient. In contrast, existing approaches rely on semidefinite programming or programming on the entropy cone, whose memory requirements can scale as $d^4$ in the underlying Hilbert-space dimension. Our method reduces this requirement to $d^2$. A minimal implementation of our algorithm takes fewer than 100 lines of Common Lisp. We demonstrate real-time key-rate estimation on a Raspberry Pi with a 1 GB memory and a Cortex-A53 processor. Despite these modest resources, our implementation outperforms existing workstation-based benchmarks by several orders of magnitude. Non-numerical verification can be incorporated with little overhead using rational approximations. These results open the way toward embedding complete numerical security analysis directly into qkd hardware.
发表机构
- Institute for Theoretical Physics, Leibniz Universität Hannover（汉诺威莱布尼茨大学理论物理研究所）
- Chair of Data Security and Cryptography, University of Regensburg（雷根斯堡大学数据安全和密码学教席）
- Institute for Quantum Information, RWTH Aachen University（亚琛工业大学量子信息研究所）
机构由 AI 辅助整理，请以论文原文为准。
