Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Third Law of Thermodynamics02:38

Third Law of Thermodynamics

18.9K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
18.9K
Entropy02:39

Entropy

30.1K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
30.1K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.8K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.8K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.3K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
5.3K
Entropy and Solvation02:05

Entropy and Solvation

7.1K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
7.1K
Quantum Numbers02:43

Quantum Numbers

34.7K
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.
34.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Metastable dynamical computing with energy landscapes: A primer.

Chaos (Woodbury, N.Y.)·2026
Same author

Way More than the Sum of Their Parts: From Statistical to Structural Mixtures.

Entropy (Basel, Switzerland)·2026
Same author

Unsupervised discovery of extreme weather events using universal representations of emergent organization.

Chaos (Woodbury, N.Y.)·2025
Same author

Intrinsic and Measured Information in Separable Quantum Processes.

Entropy (Basel, Switzerland)·2025
Same author

Controlled erasure as a building block for universal thermodynamically robust superconducting computing.

Chaos (Woodbury, N.Y.)·2025
Same author

Enumerating Finitary Processes.

Entropy (Basel, Switzerland)·2025
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 29, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K

Maximum Geometric Quantum Entropy.

Fabio Anza1,2, James P Crutchfield2

  • 1Department of Mathematics Informatics and Geoscience, University of Trieste, Via Alfonso Valerio 2, 34127 Trieste, Italy.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

Researchers propose a new principle to uniquely select quantum ensembles. The Maximum Geometric Quantum Entropy Principle identifies the ensemble maximizing geometric quantum entropy, offering a novel approach to quantum information inference.

Keywords:
density matrixgeometric quantum mechanicsmaximum entropy estimationquantum mechanics

More Related Videos

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.5K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

9.6K

Related Experiment Videos

Last Updated: Jun 29, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.5K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

9.6K

Area of Science:

  • Quantum Information Theory
  • Statistical Mechanics
  • Quantum Foundations

Background:

  • Density matrices can be represented by infinite ensembles of pure states.
  • Selecting a unique ensemble from these possibilities is a fundamental challenge.
  • Jaynes' information-theoretic perspective frames this as an inference problem.

Purpose of the Study:

  • To propose a principle for uniquely selecting quantum ensembles.
  • To leverage Quantum Information Dimension and Geometric Quantum Entropy for ensemble selection.
  • To address the inference problem of choosing a specific ensemble representation.

Main Methods:

  • Formulation of the Maximum Geometric Quantum Entropy Principle.
  • Quantification of entropy for arbitrary ensembles using Quantum Information Dimension and Geometric Quantum Entropy.
  • Mathematical formulation and analytical solution of the maximization problem.

Main Results:

  • A method to quantify the entropy of any ensemble is presented.
  • The principle identifies the unique ensemble that maximizes geometric quantum entropy.
  • Analytical solutions for the maximization problem are derived for several cases.

Conclusions:

  • The Maximum Geometric Quantum Entropy Principle provides a unique selection criterion for quantum ensembles.
  • This principle offers insights into the physical mechanisms underlying maximum entropy ensembles.
  • The study advances the understanding of quantum information inference and ensemble representations.