Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

656
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...
656
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.1K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.1K
Random Sampling Method01:09

Random Sampling Method

15.9K
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...
15.9K
Random Variables01:09

Random Variables

19.2K
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...
19.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

8.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
8.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.2K
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...
2.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Tryptophan degradation by intestinal Bacteroides induces anti-tumor immunity and limits melanoma growth.

bioRxiv : the preprint server for biology·2026
Same author

A searchable metadata network graph for microbiome metabolomics.

bioRxiv : the preprint server for biology·2026
Same author

Catalytic Activation of Bell Nonlocality.

Physical review letters·2025
Same author

Device-Independent Quantum Key Activation.

Physical review letters·2025
Same author

Bell Nonlocality in Quantum Networks with Unreliable Sources: Loophole-Free Postelection via Self-Testing.

Physical review letters·2025
Same author

Intracranial Pressure Monitor Insertion in Isolated Traumatic Brain Injury: Does Timing Matter?

The American surgeon·2025
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Apr 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Self-testing quantum random number generator.

Tommaso Lunghi1, Jonatan Bohr Brask2, Charles Ci Wen Lim1

  • 1Group of Applied Physics, Université de Genève, 1211 Genève, Switzerland.

Physical Review Letters
|May 2, 2015
PubMed
Summary
This summary is machine-generated.

We developed a self-testing protocol for quantum random number generation, allowing real-time entropy monitoring. This ensures high-quality randomness from devices without needing detailed characterization, offering a practical solution for trusted but error-prone systems.

More Related Videos

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

15.6K

Related Experiment Videos

Last Updated: Apr 13, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

15.6K

Area of Science:

  • Quantum information science
  • Cryptography
  • Information theory

Background:

  • Random number generation is crucial for science and security.
  • Estimating entropy is a key challenge in both classical and quantum randomness generation.
  • Existing methods often require extensive device characterization.

Purpose of the Study:

  • To present a novel protocol for self-testing quantum random number generation (QRNG).
  • To enable real-time monitoring of randomness entropy.
  • To provide a practical method for generating high-quality random numbers from potentially imperfect devices.

Main Methods:

  • Development of a self-testing protocol for QRNG.
  • Real-time entropy estimation during the random number generation process.
  • Implementation using a fully optical experimental setup.

Main Results:

  • Demonstration of a protocol that guarantees continuous high-quality randomness.
  • Successful real-time monitoring of entropy.
  • Validation of the self-testing capacity through optical implementation.

Conclusions:

  • The protocol offers a practical approach to quantum randomness generation.
  • It ensures reliable randomness even with trusted but error-prone devices.
  • No detailed device characterization is required, simplifying implementation.