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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.
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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.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
555
Space Trusses01:25

Space Trusses

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A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. The space truss is widely used in various construction projects due to its adaptability and capacity to withstand complex loads.
At the core of a space truss lies the fundamental unit known as the tetrahedron. This structure is composed of six members that form a three-dimensional shape...
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Transfer Function to State Space01:23

Transfer Function to State Space

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
795
State Space to Transfer Function01:21

State Space to Transfer Function

576
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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量子強化機能の空間を備えた監督学習

Vojtěch Havlíček1,2, Antonio D Córcoles3, Kristan Temme4

  • 1IBM T. J. Watson Research Center, Yorktown Heights, NY, USA.

Nature
|March 15, 2019
PubMed
まとめ
この要約は機械生成です。

この研究は,機械学習の分類のための2つの量子アルゴリズムを導入し,大型機能空間とサポートベクトルマシン (SVM) の計算的に高価なカーネル推定の制限を克服する量子コンピューティングの可能性を活用します. これらの方法は,機能表現の強化のために量子状態空間を利用し,機械学習のタスクにおける量子優位性への道を開きます.

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科学分野:

  • 量子コンピューティング
  • 機械学習
  • 指導された学習

背景:

  • サポートベクトルマシン (SVM) のような機械学習のカーネル方法は,大きな機能空間と計算的に高価なカーネル関数推定で課題に直面します.
  • 量子コンピューティングは,関わりと干渉を通じて指数関数的に大きな量子状態空間を利用することで,潜在的計算速度アップを提供します.
  • 量子コンピューティングと機械学習を結びつけることは 複雑な計算問題に取り組む上で 極めて重要です

研究 の 目的:

  • 監督学習による分類作業のための2つの新しい量子アルゴリズムを提案し,実験的に実装する.
  • 機械学習における潜在的量子優位性のための量子強化機能空間の使用を調査する.
  • 機械学習にノイイシー・インターミディアム・スケール・量子コンピュータ (NISQ) の適用を調査する.

主な方法:

  • 超伝導プロセッサで2つの量子アルゴリズムの実験的な実装.
  • 量子状態空間を拡張された機能空間として利用し,量子コンピュータでのみ効率的にアクセスできます.
  • 1つの方法は,古典的なSVMと同様の,変数量子回路を使用する量子変数分類器を使用します.
  • 2番目の方法は,クラシックSVMを最適化するために,量子コンピュータ上のカーネル関数を計算する量子カーネル推定器を含む.

主要な成果:

  • 分類のための2つの量子アルゴリズムの実験的な実装が成功しました.
  • 量子優位性への道として 量子強化特性の空間を実証する
  • NISQデバイスを機械学習の問題に適用するためのツールの開発.

結論:

  • 量子アルゴリズムは,古典的な機械学習の限界,特に大きな特性の空間に対して効果的に対処することができます.
  • 機械学習における量子優位性の実現に 有望なアプローチです
  • 開発された方法は,機械学習アプリケーションでNISQコンピュータを活用するための実用的なツールを提供します.