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相关概念视频

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Random Variables01:09

Random Variables

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...
Central Limit Theorem01:14

Central Limit Theorem

The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
Random Error01:04

Random Error

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...
The Bell Curve01:21

The Bell Curve

The normal probability distribution, often depicted as a symmetrical, bell-shaped curve, is fundamental in statistics and the study of natural phenomena. This pattern, famously described by mathematician Carl Friedrich Gauss, shows how data points are distributed around a central mean, with most values near the average and fewer observations occurring as they deviate further from it.
This pattern applies to many human characteristics beyond intelligence, such as height. For example, if you...
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Critical Numbers and the Closed Interval Method

Understanding the maximum and minimum values of a function is essential for analyzing its overall behavior. These values, often referred to as extrema, provide insight into how a function behaves across its domain. In mathematical terms, extrema can be either local—representing peaks and valleys within a limited region—or absolute, indicating the highest or lowest points over an entire interval.A function’s extrema occur at critical numbers, which are values in the domain where the derivative...

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相关实验视频

Updated: Jun 13, 2026

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
07:56

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

通过贝尔定理证明的随机数.

S Pironio1, A Acín, S Massar

  • 1Laboratoire d'Information Quantique, CP 225, Université Libre de Bruxelles, Bvd Du Triomphe, 1050 Bruxelles, Belgium.

Nature
|April 16, 2010
PubMed
概括
此摘要是机器生成的。

量子纠现在可以证明真正的随机性,使得无设备假设的安全随机数生成成为可能. 这一突破使用纠的粒子和贝尔不等式违反可靠的,不可预测的随机数.

相关实验视频

Last Updated: Jun 13, 2026

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
07:56

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

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
  • 量子力学的基础 量子力学的基础

背景情况:

  • 随机性对于密码学和模拟等应用至关重要,但生成真正不可预测的随机数字是具有挑战性的.
  • 由于理论建模的不准确性或设备漏洞,现有的随机数生成器可能不可靠.
  • 独立于设备的量子信息处理提供了一种途径,通过依赖基本的量子原理来克服这些局限性.

研究的目的:

  • 为了证明纠量子粒子的非局部相关性可以证明真正的随机性.
  • 设计一个密码安全的随机数生成器,独立于设备的内部工作.
  • 用纠的原子和贝尔不等式违规来实验验证理论建议.

主要方法:

  • 利用了两个纠在一起的原子的非局部相关性,它们之间的距离约为一米.
  • 进行测量以观察违反贝尔不等式的情况.
  • 利用贝尔不等式的违反来证明存在真正的随机性.

主要成果:

  • 在接近完美的检测效率下实现了贝尔不等式违规.
  • 保证生成42个新的随机数字,可获得99%的可靠性.
  • 展示了设备独立随机生成的概念验证.

结论:

  • 纠的量子粒子可以用来证明真正的随机性,使设备独立的随机数生成成为可能.
  • 这种方法提供了一个加密安全的方法来生成随机数字,没有对设备内部机制的假设.
  • 结果为未来的设备独立量子信息实验铺平了道路,并解决了量子随机性的基本方面.