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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

681
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...
681
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
69
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

517
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
517
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
53
Probability Distributions01:32

Probability Distributions

6.9K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.9K
Genetic Drift03:33

Genetic Drift

39.7K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
39.7K

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

Anxiety and depression among survivors 5 years after the Lushan earthquake: a large cross-sectional clinical diagnostic study.

BMC psychiatry·2026
Same author

Holographic deep thermalization for secure and efficient quantum random state generation.

Nature communications·2025
Same author

Dynamical transition in controllable quantum neural networks with large depth.

Nature communications·2024
Same author

4-Hydroxy-2-pyridone derivatives with antitumor activity produced by mangrove endophytic fungus Talaromyces sp. CY-3.

European journal of medicinal chemistry·2024
Same author

A Marine Natural Product, Harzianopyridone, as an Anti-ZIKV Agent by Targeting RNA-Dependent RNA Polymerase.

Molecules (Basel, Switzerland)·2024
Same author

Dapoxetine, a Selective Serotonin Reuptake Inhibitor, Suppresses Zika Virus Infection In Vitro.

Molecules (Basel, Switzerland)·2023

相关实验视频

Updated: Jun 30, 2025

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

543

通过否认扩散概率模型进行生成量子机器学习.

Bingzhi Zhang1,2, Peng Xu3, Xiaohui Chen4

  • 1Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA.

Physical review letters
|March 22, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了量子无声扩散概率模型 (QuDDPM),用于高效的量子数据生成. 这种模型避免了训练问题,并有效地学习复杂的量子数据,包括噪声模型和多体相.

更多相关视频

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jun 30, 2025

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

543
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

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

背景情况:

  • 深度生成模型,包括无声扩散概率模型 (DDPMs),对于计算机视觉和自然语言处理等任务至关重要.
  • 量子生成模型利用量子现象,如纠和叠加来学习量子数据.
  • 当前的量子生成模型在有效的训练和表达性方面面临挑战.

研究的目的:

  • 提出一个量子否定概率模型 (QuDDPM) 进行量子数据的高效和多功能生成式学习.
  • 在量子生成模型中解决培训效率低下和荒的高原问题.
  • 证明模型在学习复杂量子数据结构方面的能力.

主要方法:

  • 开发了 QuDDPM,灵感来自经典的 DDPM,结合了足够的电路层来实现表现力.
  • 介绍了中间培训任务,作为目标量子数据分布和噪声之间的插值.
  • 分析了学习误差边界,并对各种量子数据学习任务验证了模型.

主要成果:

  • 通过避免荒的高原问题,QuDDPM证明了高效的训练能力.
  • 该模型成功地学习了相关的量子噪声模型.
  • QuDDPM有效地捕获量子多体相和量子数据的拓结构.

结论:

  • QuDDPM为量子生成学习提供了一个多功能和高效的范式.
  • 拟议的方法使复杂量子数据的高质量生成和学习成为可能.
  • 这项工作为量子信息科学中生成模型的先进应用铺平了道路.