Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Sampling Theorem01:15

Sampling Theorem

1.2K
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.
1.2K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

663
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...
663
Sampling Distribution01:12

Sampling Distribution

16.5K
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...
16.5K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
7.2K
Sampling Methods: Overview01:06

Sampling Methods: Overview

2.1K
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...
2.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
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...
1.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Evaluating Ground State Energies of Chemical Systems with Low-Depth Quantum Circuits and High Accuracy.

The journal of physical chemistry. A·2025
Same author

Measuring the Tangle of Three-Qubit States.

Entropy (Basel, Switzerland)·2020
Same author

N-acetyl cysteine and penicillamine induce apoptosis via the ER stress response-signaling pathway.

Molecular carcinogenesis·2009
Same author

Targeting glucosylceramide synthase downregulates expression of the multidrug resistance gene MDR1 and sensitizes breast carcinoma cells to anticancer drugs.

Breast cancer research and treatment·2009
Same author

N-glycosylation of ATF6beta is essential for its proteolytic cleavage and transcriptional repressor function to ATF6alpha.

Journal of cellular biochemistry·2009
Same author

A humanized anti-osteopontin antibody inhibits breast cancer growth and metastasis in vivo.

Cancer immunology, immunotherapy : CII·2009

相关实验视频

Updated: Jan 10, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.6K

变量量子生成建模通过采样可调的可观测的预期值.

Kevin Shen1,2,3, Andrii Kurkin1,2,3, Adrián Pérez-Salinas1,4

  • 1aQaL Applied Quantum Algorithms, Leiden University, Leiden, The Netherlands.

NPJ quantum information
|November 21, 2025
PubMed
概括
此摘要是机器生成的。

量子生成模型称为预期值采样 (EVSs) 可能是资源密集型的. 可观察调节的EVS (OT-EVS) 增强了表达力,并减少了样本的复杂性,以实现高效的量子生成建模.

关键词:
量子信息是一种量子信息.量子物理学的量子物理学

更多相关视频

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

15.0K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.2K

相关实验视频

Last Updated: Jan 10, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

9.6K
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

15.0K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.2K

科学领域:

  • 量子计算是一种量子计算.
  • 机器学习是机器学习.
  • 生成型模型是一种生成型模型.

背景情况:

  • 预期值采样器 (EVS) 是学习连续分布的量子生成模型.
  • 标准的EVS通常需要大量的量子资源,这限制了它们的实际应用.

研究的目的:

  • 调查可观测的选择对EVS性能的影响.
  • 提出一个改进的EVS,增强表达力和减少资源需求.

主要方法:

  • 引入了一个可观察调节的预期值采样器 (OT-EVS).
  • 利用经典的影子测量来减少样本的复杂性.
  • 开发了一种对抗性训练方法,优先考虑经典更新.

主要成果:

  • 与标准EVS相比,OT-EVS表现出更大的表现力.
  • 提出的方法显著降低了样本的复杂性.
  • 数字实验证实了该模型的效率和表现力优势.

结论:

  • 可观测的选择对EVS性能至关重要.
  • OT-EVS为量子生成建模提供了一种更节省资源的方法.
  • 这项工作鼓励进一步探索具有较低量子资源需求的连续生成模型.