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

Sampling Theorem01:15

Sampling Theorem

1.6K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.6K
Random Sampling Method01:09

Random Sampling Method

16.0K
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...
16.0K
Sampling Methods: Overview01:06

Sampling Methods: Overview

4.2K
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...
4.2K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.8K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.8K
Sampling Distribution01:12

Sampling Distribution

20.4K
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...
20.4K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

896
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
896

You might also read

Related Articles

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

Sort by
Same author

First Ionization Energy as the Asymptotic Limit of the Average Local Electron Energy.

Journal of chemical theory and computation·2020
Same author

What Is the Accuracy Limit of Adiabatic Linear-Response TDDFT Using Exact Exchange-Correlation Potentials and Approximate Kernels?

Journal of chemical theory and computation·2019
Same author

Visualizing atomic sizes and molecular shapes with the classical turning surface of the Kohn-Sham potential.

Proceedings of the National Academy of Sciences of the United States of America·2018
Same author

Construction of Fermi Potentials from Electronic Wave Functions.

Journal of chemical theory and computation·2018
Same author

Exact exchange-correlation potentials of singlet two-electron systems.

The Journal of chemical physics·2017
Same author

Improved method for generating exchange-correlation potentials from electronic wave functions.

The Journal of chemical physics·2017

Related Experiment Video

Updated: Apr 18, 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

A pure-sampling quantum Monte Carlo algorithm.

Egor Ospadov1, Stuart M Rothstein1

  • 1Departments of Chemistry and Physics, Brock University, St. Catharines, Ontario L2S 3A1, Canada.

The Journal of Chemical Physics
|January 17, 2015
PubMed
Summary
This summary is machine-generated.

A new pure-sampling quantum Monte Carlo algorithm accurately calculates molecular properties, overcoming limitations of reptation quantum Monte Carlo (RQMC) for reliable computational chemistry. This method reduces biases for precise energy and polarizability calculations.

More Related Videos

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

15.2K

Related Experiment Videos

Last Updated: Apr 18, 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
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

15.2K

Area of Science:

  • Computational Chemistry
  • Quantum Monte Carlo Methods
  • Molecular Property Calculations

Background:

  • Pure-sampling quantum Monte Carlo aims for property calculations independent of the importance sampling function.
  • Existing methods like reptation quantum Monte Carlo (RQMC) have efficiency and accuracy limitations.

Purpose of the Study:

  • To develop and validate a novel pure-sampling algorithm for accurate quantum Monte Carlo calculations.
  • To compare the efficiency and accuracy of the new algorithm against RQMC.

Main Methods:

  • A new pure-sampling algorithm combining forward walking and RQMC features was developed.
  • The algorithm samples mixed and pure distributions simultaneously over a single set of time-steps.
  • Properties were converged by systematically increasing an algorithmic parameter.

Main Results:

  • The new algorithm demonstrated higher efficiency compared to RQMC.
  • Calculated fixed-node energy, static polarizability, and one-electron expectation values for LiH and water.
  • Results showed excellent agreement with accepted values, free from common computational biases.

Conclusions:

  • The developed pure-sampling algorithm provides accurate and unbiased molecular properties.
  • This method offers a more efficient alternative to RQMC for quantum Monte Carlo simulations.
  • The approach is validated for calculating ground-state properties of small molecules.