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

Thermodynamic Systems01:06

Thermodynamic Systems

5.0K
A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
Consider an example of  tea boiling in a kettle. The...
5.0K
First Law Of Thermodynamics: Problem-Solving01:21

First Law Of Thermodynamics: Problem-Solving

2.6K
The first law of thermodynamics states that the change in internal energy of the system is equal to the net heat transfer into the system minus the net work done by the system. This equation is a generalized form of energy conservation and can be applied to any thermodynamic process.
The following strategies can be used to solve any problem involving the first law of thermodynamics.
2.6K
Thermodynamic Potentials01:26

Thermodynamic Potentials

788
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
788
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

23.4K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic...
23.4K
Statements of the Second Law of Thermodynamics01:15

Statements of the Second Law of Thermodynamics

2.6K
The second law of thermodynamics can be stated in several different ways, and all of them can be shown to imply the others. The Clausius’ statement of the second law of thermodynamics is based on the irreversibility of spontaneous heat flow. It states that heat will not flow from the colder body to the hotter body unless some other process is involved. Additionally, as per the Kelvin’s statement, it is impossible to convert the heat from a single source into work without any other...
2.6K
Zeroth Law of Thermodynamics01:14

Zeroth Law of Thermodynamics

4.9K
Experimentally, if object A is in equilibrium with object B, and object B is in equilibrium with object C, then object A is in equilibrium with object C. That statement of transitivity is called the "zeroth law of thermodynamics." For example, a cold metal block and a hot metal block are both placed on a metal plate at room temperature. Eventually, the cold block and the plate will be in thermal equilibrium. In addition, the hot block and the plate will be in thermal equilibrium.
4.9K

You might also read

Related Articles

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

Sort by
Same author

Probing coherent quantum thermodynamics using a trapped ion.

Nature communications·2024
Same author

Geometric Optimisation of Quantum Thermodynamic Processes.

Entropy (Basel, Switzerland)·2020
Same author

Experimentally reducing the quantum measurement back action in work distributions by a collective measurement.

Science advances·2019
Same journal

Erratum for the Research Article "Assessing the health risks of rice cadmium content standards in China" by H. Chu <i>et al</i>.

Science advances·2026
Same journal

Erratum for the Research Article "Developmental regulation of Erk signaling by mitotic kinases" by F. Chen <i>et al</i>.

Science advances·2026
Same journal

Magnetically levitated metasurface enabling tangible and bidirectional human-machine interaction.

Science advances·2026
Same journal

A general photoinduced manganese-catalyzed platform for the sequential difunctionalization of [1.1.1]propellane.

Science advances·2026
Same journal

Turning sound and force into light with AlN:Mn<sup>2+</sup> mechanoluminescence.

Science advances·2026
Same journal

Extreme dominance of Earth-origin heavy ions in the intense ring current near the Earth during the May 2024 super geomagnetic storm.

Science advances·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2025

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

503

Thermodynamic computing via autonomous quantum thermal machines.

Patryk Lipka-Bartosik1, Martí Perarnau-Llobet1, Nicolas Brunner1

  • 1Department of Applied Physics, University of Geneva, 1211 Geneva, Switzerland.

Science Advances
|September 4, 2024
PubMed
Summary
This summary is machine-generated.

We introduce a physics-based model for classical computation using autonomous quantum thermal machines. These "thermodynamic neurons" leverage heat flow for computing, enabling neural network implementation.

More Related Videos

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
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

14.6K

Related Experiment Videos

Last Updated: Jun 14, 2025

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

503
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
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

14.6K

Area of Science:

  • Quantum physics
  • Thermodynamics
  • Computational science

Background:

  • Classical computation faces limitations in energy efficiency and scalability.
  • Quantum thermal machines offer novel approaches to information processing.

Purpose of the Study:

  • To develop a physics-based model for classical computation using quantum thermal machines.
  • To demonstrate the potential of thermodynamic computing for implementing neural networks.

Main Methods:

  • Modeling autonomous quantum thermal machines with interacting qubits and multiple thermal environments.
  • Exploiting heat flow and nonequilibrium steady states for computation.
  • Defining a
  • thermodynamic neuron
  • capable of implementing linearly separable functions.

Main Results:

  • A single thermodynamic neuron can perform logical operations like NOT, 3-MAJORITY, and NOR gates.
  • Networks of thermodynamic neurons can implement any arbitrary function.
  • The model establishes a direct link between quantum thermal machines and artificial neural networks.

Conclusions:

  • This work presents a novel platform for thermodynamic computing.
  • The proposed model offers a physics-based analog implementation of neural networks.
  • Quantum thermal machines provide a promising avenue for future computational paradigms.