论文标题
优化诱饵状态BB84 QKD协议参数
Optimizing the Decoy-State BB84 QKD Protocol Parameters
论文作者
论文摘要
QKD实施的性能取决于基础安全分析的紧密性。特别是,安全分析确定了密钥速率,即每个时间单元可以分配的加密密钥材料的量。如今,对各种QKD协议的安全分析已被充分理解。众所周知,可以通过解决非线性优化问题来找到最佳协议参数,例如诱饵状态及其强度的数量。这种优化问题的复杂性通常是通过做出许多启发式假设来处理的。例如,诱饵状态的数量仅限于一个或两个,其中一个诱饵强度设置为固定值,而真空状态被忽略,因为它们被认为仅对安全的键率略有贡献。这些假设简化了优化问题,并大大减少了搜索空间的大小。但是,它们还导致安全分析是不紧密的,从而导致了次优的性能。 在这项工作中,我们使用线性和非线性程序遵循更严格的方法,描述了优化问题。我们的方法着眼于诱饵状态BB84协议,可以省略启发式假设,因此可以通过更好的协议参数进行更严格的安全性分析。我们显示了诱饵状态BB84 QKD方案的改进性能,表明通常做出的启发式假设过于限制。此外,我们改进的优化框架表明,即使有限的计算资源可用,也可以处理性能优化问题的复杂性,而无需做出启发式假设。
The performance of a QKD implementation is determined by the tightness of the underlying security analysis. In particular, the security analyses determines the key-rate, i.e., the amount of cryptographic key material that can be distributed per time unit. Nowadays, the security analyses of various QKD protocols are well understood. It is known that optimal protocol parameters, such as the number of decoy states and their intensities, can be found by solving a nonlinear optimization problem. The complexity of this optimization problem is typically handled by making an number of heuristic assumptions. For instance, the number of decoy states is restricted to only one or two, with one of the decoy intensities set to a fixed value, and vacuum states are ignored as they are assumed to contribute only marginally to the secure key-rate. These assumptions simplify the optimization problem and reduce the size of search space significantly. However, they also cause the security analysis to be non-tight, and thereby result in sub-optimal performance. In this work, we follow a more rigorous approach using both linear and non-linear programs describing the optimization problem. Our approach, focusing on the Decoy-State BB84 protocol, allows heuristic assumptions to be omitted, and therefore results in a tighter security analysis with better protocol parameters. We show an improved performance for the Decoy-State BB84 QKD protocol, demonstrating that the heuristic assumptions typically made are too restrictive. Moreover, our improved optimization frameworks shows that the complexity of the performance optimization problem can also be handled without making heuristic assumptions, even with limited computational resources available.