地图化保证了连续变量量子密钥分布的积极秘密密钥率
Mikhael T Sayat1,2,3,4, Oliver Thearle2,5, Biveen Shajilal3,4
1Department of Physics, Faculty of Science, University of Auckland, Auckland 1010, New Zealand.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
本研究引入了一个新的工具,通过同时考虑多个参数来评估连续变量量子密钥分布 (CVQKD) 协议. 与M-APSK和M-PSK协议相比,M-QAM协议在生成安全量子密钥方面表现出卓越的性能.
科学领域:
- 量子信息科学 量子信息科学
- 量子密码学 量子密码学
- 光学通信系统 光学通信系统
背景情况:
- 传统的连续变量量子密钥分布 (CVQKD) 性能评估使用单参数分析,忽视相互依赖.
- 这种原子主义的方法不足以理解在现实的,波动的通道条件下协议功能.
研究的目的:
- 开发一个数字工具,用于全面的CVQKD协议比较.
- 分析多个参数对秘密密钥率 (SKR) 生成的同时影响.
- 为不稳定的量子通信通道确定最佳的CVQKD协议.
主要方法:
- 开发了一个数字工具来评估CVQKD协议,同时考虑多个参数.
- 在传导率,过量噪声和调制幅度的参数空间内,正的秘密密钥速率 (SKR) 的映射区域.
- 我们比较了三个离散调制 (DM) CVQKD 协议:M-QAM,M-APSK 和 M-PSK.
主要成果:
- 发现M-QAM协议在产生积极的SKR方面表现优于M-APSK和M-PSK协议.
- 观察到SKR生成能力的非线性增长,连贯状态的数量越来越多.
- 确定了特定的参数空间区域,协议可以实现积极的SKR.
结论:
- 开发的工具提供了比传统方法更全面的CVQKD协议性能评估.
- M-QAM协议是一个有前途的候选人,可以在动态环境中进行安全通信,例如自由空间光学.
- 这种整体方法有助于选择最佳的CVQKD协议,用于具有波动道特征的真实应用.
相关概念视频
BIBO stability of continuous and discrete -time systems
353
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
353
Quantum Numbers
34.4K
It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
34.4K
Probability Distributions
6.8K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Propagation of Uncertainty from Random Error
654
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
654
Poisson Probability Distribution
7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
The...
7.8K
Binomial Probability Distribution
10.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.2K


