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Related Concept Videos

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

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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?
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Related Experiment Video

Updated: Mar 29, 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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The MadQCI Cloud Scenario: Quantum as a Service.

Jaime S Buruaga1, Alberto Sebastián-Lombraña1,2, Ruben B Méndez1

  • 1Center for Computational Simulation, Universidad Politécnica de Madrid, 28660 Madrid, Spain.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

Madrid Quantum Communication Infrastructure (MadQCI) offers cloud-based quantum services, simplifying access for researchers. This approach abstracts complexity, enabling users to leverage quantum technologies like QKD and QRNG without deep technical knowledge.

Keywords:
post-quantum cryptographyquantum as a servicequantum key distributionquantum random number generatorquantum technologies

Related Experiment Videos

Last Updated: Mar 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Area of Science:

  • Quantum communication infrastructure
  • Cloud computing
  • Quantum technology services

Background:

  • The Madrid Quantum Communication Infrastructure (MadQCI) aims to foster the quantum technology scientific community.
  • Existing quantum networks often require specialized knowledge, limiting accessibility for end users.
  • There is a need to simplify the delivery and usage of quantum communication primitives and services.

Purpose of the Study:

  • To expose quantum services in a user-friendly manner within the MadQCI.
  • To abstract the underlying technical complexity of quantum services for end users.
  • To investigate the delivery of quantum-enabled services using a cloud-based paradigm.

Main Methods:

  • Commissioning a cloud-like, quantum-enabled network scenario.
  • Offering quantum services following the "anything as a service" (XaaS) model.
  • Implementing services such as SD-QKD, QRNG, Quantum-Safe TLS, and Quantum-Safe IPsec as a Service.

Main Results:

  • Successful deployment of a user-friendly interface for accessing quantum services.
  • Demonstration of the XaaS model for delivering quantum communication primitives.
  • Enabling end users to operate quantum services without prior implementation knowledge.

Conclusions:

  • The cloud-based paradigm effectively abstracts complexity for quantum services.
  • The MadQCI scenario promotes broader adoption and growth of quantum technology.
  • The XaaS model is a transferable approach for delivering quantum-enabled services.