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Quantum Numbers02:43

Quantum Numbers

34.0K
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.
34.0K
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...
41.6K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.3K
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).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

26
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
26
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

225
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
225
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

2.9K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
2.9K

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Related Experiment Video

Updated: May 10, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Quantum global minimum finder based on variational quantum search.

Mohammadreza Soltaninia1, Junpeng Zhan2

  • 1Department of Electrical Engineering, Alfred University, Alfred, NY, 14802, USA.

Scientific Reports
|April 22, 2025
PubMed
Summary
This summary is machine-generated.

We developed the Quantum Global Minimum Finder (QGMF), a quantum computing method to efficiently find global minima in complex optimization problems. QGMF uses binary search and Variational Quantum Search for enhanced scalability and effectiveness.

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Area of Science:

  • Quantum Computing
  • Optimization Algorithms
  • Computational Science

Background:

  • Global optimization is challenging due to non-convex functions with multiple local optima.
  • Fields like engineering, finance, and AI struggle with complex optimization tasks.
  • Existing methods face limitations in efficiently finding global minima.

Purpose of the Study:

  • Introduce a novel quantum computing approach for global minimum identification.
  • Address the limitations of classical optimization for non-convex functions.
  • Demonstrate the efficacy of quantum computing in solving complex optimization problems.

Main Methods:

  • Developed the Quantum Global Minimum Finder (QGMF).
  • Integrated binary search techniques to position the objective function.
  • Employed Variational Quantum Search to pinpoint the global minimum.
  • Utilized a O(n)-depth circuit architecture for efficiency.

Main Results:

  • QGMF efficiently identifies global minima in non-convex functions.
  • The approach leverages binary search for enhanced scalability.
  • Achieved precise global minimum localization within targeted subspaces.
  • Demonstrated improved efficiency through logarithmic benefits of binary search.

Conclusions:

  • QGMF represents a significant advancement in quantum-assisted optimization.
  • Quantum computing offers effective solutions for complex non-convex optimization.
  • The QGMF method enhances the capabilities of quantum computing for practical applications.