Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Sampling Theorem01:15

Sampling Theorem

1.4K
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.
1.4K
Sampling Methods: Overview01:06

Sampling Methods: Overview

3.6K
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...
3.6K
Upsampling01:22

Upsampling

656
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
656
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

775
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...
775
Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.1K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.4K
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...
3.4K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Consistent inclusion of triple substitutions within a coupled cluster based static quantum embedding theory.

The Journal of chemical physics·2026
Same author

Independent association of leg-height ratio with 15 cardiometabolic diseases.

Cardiovascular diabetology·2026
Same author

Retraction of: Esophageal carcinoma cell-excreted exosomal uc.189 promotes lymphatic metastasis.

Aging·2025
Same author

Editorial: Global infectious disease surveillance technologies and data sharing protocols.

Frontiers in public health·2025
Same author

Optimized Auxiliary Functions for Robust Mitigation of Finite-Size Errors in Periodic Hybrid Density Functional Theory.

Journal of chemical theory and computation·2025
Same author

Renormalization of states and quasiparticles in many-body downfolding.

The Journal of chemical physics·2025
Same journal

Chemotactic self-organization captures the dynamics of mammalian hair follicle patterning.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Tomographic imaging of superconducting order using particle-hole interference.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Analytical modeling for suction cup designs for skin-interfaced wearable devices.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Improving cell-free metabolism through direct integration of artificial respiratory chains.

Proceedings of the National Academy of Sciences of the United States of America·2026
関連記事をすべて見る

関連する実験動画

Updated: Feb 22, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.2K

オペレーターレベルの非ログコンキャブサンプリングの量子加速

Jiaqi Leng1,2, Zhiyan Ding2,3, Zherui Chen2

  • 1Simons Institute for the Theory of Computing, University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America
|February 20, 2026
PubMed
まとめ
この要約は機械生成です。

この研究は,複雑な確率分布からのサンプリングを加速する量子アルゴリズムを導入し,古典的な方法が失敗する非ログコンキャブポテンシャルに重要なスピードアップを提供します. 物理や機械学習などの分野で,より速いシミュレーションを可能にします.

キーワード:
ギブスのサンプリングランゲヴィンのダイナミクスラプラシアン語を目撃した.量子アルゴリズムの量子アルゴリズム単一価値の値を保持しています.

さらに関連する動画

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

13.4K
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

15.1K

関連する実験動画

Last Updated: Feb 22, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.2K
Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

13.4K
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

15.1K

科学分野:

  • 量子コンピューティング
  • 計算物理学の物理
  • 統計学の力学 統計学の力学
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) というものです.

背景:

  • 確率分布からのサンプリングは,様々な科学分野において極めて重要です.
  • ランゲヴィンダイナミクスのような古典的な方法は,非ログコンカブ分布と闘い,パフォーマンスを妨げます.
  • 複雑なエネルギー環境は,正確で効率的なサンプリングに重大な課題をもたらします.

研究 の 目的:

  • 連続時間サンプリングダイナミクスを加速するための量子アルゴリズムを開発する.
  • ノンログコンキャブ環境における古典的なサンプリング方法の限界に対処するために.
  • 複雑で荒れたエネルギー環境から効率的なサンプリングを可能にします.

主な方法:

  • 目標のギブス測定値を量子状態の振幅にコードする.
  • ウィッテン・ラプラシアン演算子のブロック行列因数分解を用いて.
  • 単一値の値設定によるギブスサンプリングの実施.
  • レプリカ交換の量子アルゴリズムの開発 ランゲヴィン拡散.

主要な成果:

  • 連続時間サンプリングダイナミクスの広範なクラスのための実証可能な加速.
  • 非ログコンカブ分布の古典的なランゲヴィンベースの方法に対する四次量子加速まで.
  • 複製交換を加速する最初の量子アルゴリズム ランゲヴィン拡散.

結論:

  • 開発された量子アルゴリズムは,複合分布のサンプリングに重要な利点を提供します.
  • この研究は,物理,化学,そしてそれ以上の分野でシステムをシミュレートするための強力な新しいツールを提供します.
  • 量子コンピューティングは,古典的なサンプリング技術の根本的な制限を克服することができます.