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

Quantitative Analysis01:12

Quantitative Analysis

279
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
279
Machines: Problem Solving II01:30

Machines: Problem Solving II

308
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
308
Machines: Problem Solving I01:22

Machines: Problem Solving I

315
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
315
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

99
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
99
Associative Learning01:27

Associative Learning

333
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
333
Hindsight Biases01:12

Hindsight Biases

3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
3.4K

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

From quantum feature maps to quantum reservoir computing: an applicative perspective.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Exponential quantum advantages in learning quantum observables from classical data.

NPJ quantum information·2026
Same author

Beyond reptiles: the fire salamander as a potential host for <i>Leishmania (Sauroleishmania) tarentolae</i>.

International journal for parasitology. Parasites and wildlife·2025
Same author

Development of a novel ddPCR assay for the simultaneous detection of the protozoan parasites Leishmania infantum and Leishmania tarentolae.

Parasites & vectors·2025
Same author

A novel chemically defined medium for the biotechnological and biomedical exploitation of the cell factory Leishmania tarentolae.

Scientific reports·2024
Same author

Quantum machine learning beyond kernel methods.

Nature communications·2023
Same journal

Plasmonic nanocomposite helices for weather-adaptive LiDAR function.

Nature communications·2026
Same journal

Multidirectional strain-insensitive stretchable RF electronics.

Nature communications·2026
Same journal

In-scanner thoughts contribute to resting-state functional connectivity.

Nature communications·2026
Same journal

Metal-center electron affinity modulates multicolor electrochromism in 2D conjugated metal-organic frameworks.

Nature communications·2026
Same journal

Hyperbranched dielectric polymer networks exhibiting giant energy storage density at 250 °C.

Nature communications·2026
Same journal

3D nanoprinting of metals by spatiotemporally confined hot electrons via multiple-electron excitations in nanocrystals.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: Jun 21, 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

531

量子机器学习的阴影 量子机器学习的阴影

Sofiene Jerbi1,2, Casper Gyurik3, Simon C Marshall3

  • 1Institute for Theoretical Physics, University of Innsbruck, Innsbruck, Austria. sofiene.jerbi@fu-berlin.de.

Nature communications
|July 6, 2024
PubMed
概括
此摘要是机器生成的。

量子机器学习模型现在可以在量子计算机上训练后进行经典部署. 这种方法可以为更广泛的实际应用提供量子学习优势.

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
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

相关实验视频

Last Updated: Jun 21, 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

531
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
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

科学领域:

  • 量子计算是一种量子计算.
  • 机器学习 机器学习
  • 计算复杂性 计算复杂性

背景情况:

  • 量子机器学习 (QML) 提供了计算优势,但需要量子硬件进行评估.
  • 在新数据上对训练有素的QML模型进行评估需要访问量子计算机,这限制了实际使用.

研究的目的:

  • 引入一种新的类型的QML模型,可以用量子资源进行训练,但可以用经典方式部署.
  • 通过将培训与评估脱而出,实现QML的实际应用.

主要方法:

  • 开发了一种培训方法,为经典部署提供了一个"影子模型".
  • 已被证明是经典部署的QML的普遍性.
  • 分析学习能力,并将其与完全量子和经典模型进行比较.

主要成果:

  • 建议的模型对于经典部署的QML.是通用的.
  • 与完全量子模型相比,这些模型具有有限的学习能力.
  • 根据标准复杂性假设,可以实现比经典学习者具有可证明的学习优势.

结论:

  • 量子机器学习可以提供优势,即使量子计算机仅用于培训.
  • 经典部署的QML模型扩大了它们在现实世界的场景中的适用性.
  • 这项研究通过克服评估的硬件限制,促进了QML在各种实际环境中的集成.