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Random Sampling Method01:09

Random Sampling Method

12.9K
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...
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Cluster Sampling Method01:20

Cluster Sampling Method

13.2K
Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.2K
Spin–Spin Coupling: One-Bond Coupling01:17

Spin–Spin Coupling: One-Bond Coupling

1.1K
Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...
1.1K
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)

1.2K
Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
1.2K
Sampling Methods: Overview01:06

Sampling Methods: Overview

665
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Distribution01:12

Sampling Distribution

14.9K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Related Experiment Video

Updated: Oct 20, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Approximate optimization, sampling, and spin-glass droplet discovery with tensor networks.

Marek M Rams1, Masoud Mohseni2, Daniel Eppens2

  • 1Jagiellonian University, Institute of Theoretical Physics, Łojasiewicza 11, 30-348 Kraków, Poland.

Physical Review. E
|September 16, 2021
PubMed
Summary

Researchers developed a new algorithm using tensor networks to efficiently find solutions for complex spin-glass problems. This method discovers numerous high-quality solutions for challenging optimization tasks, outperforming previous results.

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Area of Science:

  • Computational Physics
  • Quantum Information Science

Background:

  • Spin-glass systems often encode computationally hard optimization problems.
  • Representing all possible spin configurations is computationally intractable for large systems.

Purpose of the Study:

  • To develop an efficient algorithm for sampling high-quality solutions in spin-glass systems.
  • To leverage tensor networks for analyzing the low-energy spectrum of complex Hamiltonians.

Main Methods:

  • Utilized tensor networks to represent the Gibbs distribution of spin configurations.
  • Employed approximate tensor-network contractions to map the low-energy spectrum.
  • Exploited problem locality to compute spin-glass droplet geometries for spectrum compression and sampling.

Main Results:

  • Successfully mapped the low-energy spectrum of quasi-two-dimensional Hamiltonians.
  • Developed a method for sampling solutions that avoids #P-complete exact contraction.
  • Discovered approximately 10^10 degenerate ground states for deceptive cluster loops on chimera graphs with up to 2048 spins.
  • Achieved better solutions than previously reported for some hard instances.

Conclusions:

  • The gradient-free algorithm efficiently samples high-quality solutions for hard optimization problems encoded in spin glasses.
  • The method provides insights into the structure of disordered spin-glass systems.
  • Potential applications exist for machine learning and noisy intermediate-scale quantum (NISQ) devices.