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相关概念视频

Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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相关实验视频

Updated: Jan 12, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Generation and Coherent Control of Pulsed Quantum Frequency Combs

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基于上下文的最佳和可行的随机生成.

Yuan Liu1, Ravishankar Ramanathan1

  • 1The University of Hong Kong, Department of Computer Science, School of Computing and Data Science, Pokfulam Road, Hong Kong, China.

Physical review letters
|November 7, 2025
PubMed
概括
此摘要是机器生成的。

使用Kochen-Specker上下文性的半设备独立的随机生成提供了实际的优势. 这项研究引入了最佳随机性认证的新方法,并放松了假设,提高了量子信息处理的安全性和适用性.

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科学领域:

  • 量子信息科学 量子信息科学
  • 量子基础的基础 量子基础的基础
  • 量子密码学 量子密码学

背景情况:

  • 半设备独立 (SDT) 随机生成协议利用了科亨-斯佩克的上下文性.
  • 这些协议比完全独立于设备 (DI) 的协议具有优势,包括紧的设备,高速率和实验简单性.
  • 一个关键的假设是测量的可重复性和遵守预期的兼容性结构.

研究的目的:

  • 改进半设备独立随机生成的最先进技术.
  • 为最佳随机性认证引入新的实验性可行结构.
  • 放松现有的假设并分析对更广泛类别的对手的安全性.

主要方法:

  • 介绍一系列简单,实验上可行的正交线图 (测量兼容性结构).
  • 对于这些图表,Lovász theta和分数包装号的分析推导.
  • 通过 ε-正交度图表来框架测量兼容性的放松,并推导出相应的量子相关性.

主要成果:

  • 证明了特定的正交度图允许认证来自qudit系统 (d≥3) 的最大log2d位随机性.
  • 证明了这些图形对于最佳随机性扩展和放大的实用性.
  • 导出量子相关性,使得测量兼容性的任意放松 ε∈[0,1) 的随机性认证成为可能.
  • 表明单量子比特相关性几乎可以是上下文的,抵制小 ε 的忠实非上下文隐藏变量模型.
  • 确定了针对量子和一致对手的潜在安全漏洞.

结论:

  • 开发的正交线图是半设备独立设置中随机生成的最佳选择.
  • 测量兼容性的放松扩大了基于上下文的随机生成的适用性.
  • 单个量子比特表现出接近上下文的行为,对量子基础有影响.
  • 对更广泛的对手进行安全分析对于强大的量子协议至关重要.