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Quantum Numbers02:43

Quantum Numbers

49.4K
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.
49.4K
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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2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

1.4K
Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
1.4K
What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
17.6K
Variation01:19

Variation

7.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.7K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

Updated: Jan 21, 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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Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems.

Alexandra Nagy1, Vincenzo Savona1

  • 1Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.

Physical Review Letters
|July 27, 2019
PubMed
Summary

Simulating large quantum systems is challenging due to exponential complexity. This study introduces a variational neural network method for efficient simulation of open quantum systems, enabling new quantum science discoveries.

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Area of Science:

  • Quantum Information Science
  • Condensed Matter Physics
  • Computational Quantum Physics

Background:

  • Simulating many-body open quantum systems with many degrees of freedom is crucial for quantum science and information.
  • The exponential growth of the density matrix complexity with system size presents a significant computational challenge.

Purpose of the Study:

  • To develop an efficient variational method for simulating the nonequilibrium steady state of Markovian open quantum systems.
  • To leverage neural networks for representing the density matrix and variational Monte Carlo methods for efficient computation.

Main Methods:

  • A variational method is developed using neural network representations of the density matrix.
  • Variational Monte Carlo methods and the stochastic reconfiguration scheme are employed.
  • The method integrates the quantum master equation through the application of the variational principle.

Main Results:

  • The developed method efficiently simulates the nonequilibrium steady state of open quantum systems.
  • The effectiveness was demonstrated by modeling the two-dimensional dissipative XYZ spin model on a lattice.
  • The approach addresses the computational complexity associated with large quantum systems.

Conclusions:

  • This variational neural network approach offers an efficient pathway to simulate complex open quantum systems.
  • The method provides a powerful tool for tackling outstanding problems in quantum science and quantum information.
  • It paves the way for advancements in understanding and controlling quantum phenomena in larger systems.