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Quantum Numbers02:43

Quantum Numbers

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It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
52.4K
What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
59.8K
Variation01:19

Variation

8.1K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.1K
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.9K
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
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Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

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Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
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Updated: Feb 15, 2026

Production and Targeting of Monovalent Quantum Dots
10:16

Production and Targeting of Monovalent Quantum Dots

Published on: October 23, 2014

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変数量子 Eigensolver を使ってマルチクラスポートフォリオの最適化,ディッケ状態の代替で

J V S Scursulim1, Gabriel M Langeloh2, Victor L Beltran2

  • 1Instituto de Ciência e Tecnologia Itaú, São Paulo, Brazil. jose.scursulim@itau-unibanco.com.br.

Scientific reports
|February 13, 2026
PubMed
まとめ

この研究は,多様化に配慮したポートフォリオの最適化のための新しい量子フレームワークを導入しています. Dicke state ansatzは,実行可能な解決策を確保し,既存の方法よりも優れたパフォーマンスを提供することにより,パフォーマンスを大幅に向上させます.

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Gradient Echo Quantum Memory in Warm Atomic Vapor
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Gradient Echo Quantum Memory in Warm Atomic Vapor

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Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Generation and Coherent Control of Pulsed Quantum Frequency Combs

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関連する実験動画

Last Updated: Feb 15, 2026

Production and Targeting of Monovalent Quantum Dots
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Gradient Echo Quantum Memory in Warm Atomic Vapor
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科学分野:

  • 量子コンピューティング
  • コンピューティング・ファイナンス
  • オプティマイゼーション アルゴリズム

背景:

  • ポートフォリオの最適化は金融において極めて重要ですが,多様化についてはしばしば無視されます.
  • ポートフォリオ最適化のための既存の量子アルゴリズムには,明示的な多様化制約がない.
  • 現実的な金融モデルは,実用的な適用のために多様化を組み込むことを要求します.

研究 の 目的:

  • 多種多様なポートフォリオの最適化のための新しい量子フレームワークを開発し,多様化を明示的に組み込む.
  • 多様化制約を本質的に満たす変数量子エイゲンソルバー (VQE) の新しい代替案を導入する.
  • このハイブリッド量子-古典的アプローチの性能に対する古典的最適化器の影響を分析する.

主な方法:

  • 多数のパラメータ化されたディッケ状態をVQEの代替として使って,多様化制約をエンコードした.
  • 量子システムを実行可能な状態の重置で初期化し,検索スペースを縮小し,ペナルティ項の必要性を排除しました.
  • CMA-ES最適化器を中心に,さまざまな古典的な最適化器でパフォーマンスを評価しました.

主要な成果:

  • ディッケ・ステート・アンサツは,多元化制約を成功裏にコードし,実行可能なポートフォリオ状態のみを調査することを保証しました.
  • ハイブリッド量子-古典的アプローチ,特にCMA-ES最適化器は,優れた収束率,近似比率,および測定確率を示しました.
  • この方法は,ペナルティ用語を回避することで,計算上の検索スペースを大幅に削減しました.

結論:

  • 提案された量子フレームワークは,実践的で多様化に配慮したポートフォリオ最適化のための有望なソリューションを提供します.
  • ディッケ・ステート・アンサッツは,量子最適化における制約を効率的に扱うための重要な革新である.
  • このアプローチは,現実のポートフォリオ管理の課題に取り組むために,金融セクターでの応用に大きな可能性を持っています.