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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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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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Machines: Problem Solving II01:30

Machines: Problem Solving II

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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.
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Machines: Problem Solving I01:22

Machines: Problem Solving I

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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.
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Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

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Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
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Sequence Networks of Rotating Machines01:24

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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...
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Related Experiment Video

Updated: Jun 6, 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

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Quantum Machine Learning-Quo Vadis?

Andreas Wichert1

  • 1Department of Computer Science and Engineering, INESC-ID & Instituto Superior Técnico, University of Lisbon, 2744-016 Porto Salvo, Portugal.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

Quantum machine learning (QML) offers potential big data advantages but faces real-world application challenges. Constraints like data encoding bottlenecks question the feasibility of promised quantum speed-ups.

Keywords:
HHLamplitude encodingbasis encodinginput destruction problemquantum kernelsquantum machine learningvariational algorithm

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Data Mining

Background:

  • Quantum machine learning (QML) gained popularity, promising exponential speed-ups and compressed data representation for big data applications.
  • Classical machine learning algorithms face limitations when ported to quantum computing due to quantum physical constraints.
  • Key challenges include the input-output problem and normalized vector representation, hindering direct algorithm translation.

Purpose of the Study:

  • To critically evaluate the practical applicability of quantum machine learning for real-world scenarios.
  • To assess the realistic advantages of quantum machine learning algorithms beyond theoretical speed-ups.
  • To investigate the feasibility of promised exponential or quadratic speed-ups on actual quantum computers.

Main Methods:

  • Analysis of theoretical speed-ups in quantum machine learning literature.
  • Identification of quantum physical constraints impacting algorithm implementation.
  • Focus on the input destruction problem as a primary bottleneck in data encoding.

Main Results:

  • Theoretical analyses often overlook the input destruction problem, a critical barrier for data encoding in quantum machine learning.
  • Direct porting of classical algorithms is not feasible due to quantum physical constraints.
  • The practical advantages and realistic speed-ups of QML remain questionable under current constraints.

Conclusions:

  • The feasibility of applying quantum machine learning to real-world problems is uncertain due to significant implementation challenges.
  • Marginalizing or ignoring quantum physical constraints like data encoding bottlenecks may lead to unrealistic performance expectations.
  • Further research is needed to address these constraints for practical quantum machine learning advancements.