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

Correlation of Experimental Data01:23

Correlation of Experimental Data

499
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
499
Coefficient of Correlation01:12

Coefficient of Correlation

8.8K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.8K
Correlation01:09

Correlation

15.4K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
15.4K
Heating and Cooling Curves02:44

Heating and Cooling Curves

28.2K
When a substance—isolated from its environment—is subjected to heat changes, corresponding changes in temperature and phase of the substance is observed; this is graphically represented by heating and cooling curves.
For instance, the addition of heat raises the temperature of a solid; the amount of heat absorbed depends on the heat capacity of the solid (q = mcsolidΔT). According to thermochemistry, the relation between the amount of heat absorbed or released by a substance, q, and its...
28.2K
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

685
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
685
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

2.3K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Encrypted Qubits Can Be Cloned.

Physical review letters·2026
Same author

Efficiently Cooling Quantum Systems with Finite Resources: Insights from Thermodynamic Geometry.

Physical review letters·2025
Same author

No Black Holes from Light.

Physical review letters·2024
Same author

Protecting information via probabilistic cellular automata.

Physical review. E·2024
Same author

Model for Emergence of Spacetime from Fluctuations.

Physical review letters·2023
Same author

Super Interferometric Range Resolution.

Physical review letters·2023
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Feb 22, 2026

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.9K

Correlation-Enhanced Algorithmic Cooling.

Nayeli A Rodríguez-Briones1,2,3, Eduardo Martín-Martínez1,2,3,4, Achim Kempf1,2,3,4

  • 1Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.

Physical Review Letters
|September 27, 2017
PubMed
Summary
This summary is machine-generated.

We developed a new quantum cooling method that uses internal system interactions to boost purity beyond previous limits. This technique leverages quantum correlations for enhanced cooling in interacting quantum systems.

More Related Videos

Author Spotlight: Improving Lesion Contiguity in Pulmonary Vein Isolation via Proactive Esophageal Cooling
05:43

Author Spotlight: Improving Lesion Contiguity in Pulmonary Vein Isolation via Proactive Esophageal Cooling

Published on: April 19, 2024

1.5K
Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat
09:43

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat

Published on: December 11, 2017

7.3K

Related Experiment Videos

Last Updated: Feb 22, 2026

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.9K
Author Spotlight: Improving Lesion Contiguity in Pulmonary Vein Isolation via Proactive Esophageal Cooling
05:43

Author Spotlight: Improving Lesion Contiguity in Pulmonary Vein Isolation via Proactive Esophageal Cooling

Published on: April 19, 2024

1.5K
Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat
09:43

Reversible Cooling-induced Deactivations to Study Cortical Contributions to Obstacle Memory in the Walking Cat

Published on: December 11, 2017

7.3K

Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Thermodynamics

Background:

  • Algorithmic cooling is a technique to improve the purity of quantum systems.
  • Conventional methods often assume no internal interaction within the system.
  • Achieving higher purity is crucial for robust quantum computation.

Purpose of the Study:

  • To propose a novel method for enhancing the purity of interacting quantum systems.
  • To explore the potential of utilizing internal interactions and quantum correlations for cooling.
  • To surpass the cooling limits of existing algorithmic cooling techniques.

Main Methods:

  • Developing a cooling protocol tailored for interacting quantum systems.
  • Exploiting inherent quantum correlations arising from internal interactions.
  • Analyzing the cooling performance under conditions of strong internal interaction.

Main Results:

  • Demonstrated a method to increase the purity of interacting quantum systems.
  • Showcased cooling beyond established limits of conventional algorithmic cooling.
  • Quantified the advantage gained by utilizing quantum correlations in the presence of strong interactions.

Conclusions:

  • Internal interactions in quantum systems can be leveraged for enhanced cooling.
  • The proposed method offers a pathway to achieve higher purity than previously possible.
  • This approach is particularly effective for systems with strong internal interactions.