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

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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.
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Magnetic Susceptibility and Permeability01:31

Magnetic Susceptibility and Permeability

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In linear magnetic materials, like paramagnets and diamagnets, magnetization is proportional to the magnetic field intensity. The constant of proportionality, a dimensionless number, is called magnetic susceptibility. The value of the susceptibility depends on the type of material.
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Jan 16, 2026

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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Ascertaining Susceptibilities in Smart Contracts: A Quantum Machine Learning Approach.

Amulyashree Sridhar1, Kalyan Nagaraj2, Shambhavi Bangalore Ravi2

  • 1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru Campus, Bengaluru 560035, Karnataka, India.

Entropy (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

Quantum machine learning (QML) models effectively detect smart contract liabilities, outperforming traditional methods. The Quantum Neural Network (QNN) model achieved 82.43% accuracy, offering a promising solution for blockchain security.

Keywords:
McNemar’s TestQMLQNNliabilitiessmart contracts

Related Experiment Videos

Last Updated: Jan 16, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Area of Science:

  • Computer Science
  • Quantum Computing
  • Blockchain Technology

Background:

  • Smart contracts are crucial for blockchain and decentralized applications.
  • Liabilities in smart contracts can lead to system failures.
  • Current static detection tools have limitations, including false positives and inability to generalize.
  • Machine learning (ML) approaches face challenges with smart contract size, storage, and performance.

Purpose of the Study:

  • To explore Quantum Machine Learning (QML) applications for identifying liabilities in smart contracts.
  • To address the limitations of existing static analysis and ML-based methods for smart contract vulnerability detection.

Main Methods:

  • Employed four QML approaches: Quantum Neural Network (QNN), Quantum Support Vector Machine (QSVM), Variational Quantum Circuit (VQC), and Quantum Random Forest (QRF).
  • Trained and evaluated models on smart contract data to detect liabilities.
  • Validated the best-performing model on the SolidiFI dataset.
  • Utilized McNemar's test for statistical validation.

Main Results:

  • The QNN model demonstrated superior performance in detecting smart contract liabilities, achieving an accuracy of 82.43%.
  • QML approaches showed advantages over traditional ML in terms of storage and performance.
  • Consistent results were observed when the QNN model was tested on the SolidiFI dataset.
  • Statistical validation confirmed the model's performance.

Conclusions:

  • QML, particularly the QNN model, presents a viable and effective alternative for detecting smart contract liabilities.
  • QML offers improved storage and performance benefits compared to traditional ML methods for this task.
  • The findings suggest QML can enhance the security and reliability of smart contracts and decentralized systems.