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Quantifying Heat02:46

Quantifying Heat

61.6K
Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a higher temperature. When the...
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Maxwell's Thermodynamic Relations01:23

Maxwell's Thermodynamic Relations

4.4K
Maxwell's thermodynamic relations are very useful in solving problems in thermodynamics. Each of Maxwell's relations relates a partial differential between quantities that can be hard to measure experimentally to a partial differential between quantities that can be easily measured. These relations are a set of equations derivable from the symmetry of the second derivatives and the thermodynamic potentials.
All thermodynamic potentials are exact differentials. Therefore, their second-order...
4.4K
Heat Capacities of an Ideal Gas II01:23

Heat Capacities of an Ideal Gas II

3.7K
For a system that undergoes a thermodynamic process at a constant volume condition, the heat absorbed is used only to increase the system's internal energy and not for doing any kind of work. While for a system undergoing a thermodynamic process under a constant pressure condition, the amount of heat absorbed is used not only for increasing the internal energy (as a function of temperature) but also for doing some work. The molar heat capacity is the amount of heat required to increase the...
3.7K
Heat Capacities of an Ideal Gas III01:25

Heat Capacities of an Ideal Gas III

3.3K
The number of independent ways a gas molecule can move along straight line, rotate, and vibrate is called its degrees of freedom. Supposing d represents the number of degrees of freedom of an ideal gas, the molar heat capacity at constant volume of an ideal gas in terms of d is
3.3K
Thermodynamic Potentials01:26

Thermodynamic Potentials

1.5K
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...
1.5K
Heat Capacities of an Ideal Gas I01:14

Heat Capacities of an Ideal Gas I

4.2K
Heat capacity is the ratio of heat absorbed by the substance corresponding to its temperature change. It is also called thermal capacity and the SI unit of heat capacity is J/K. Whereas, specific heat capacity is defined as the amount of heat necessary to change the temperature of 1 kg of a substance by 1 K and is also called massic heat capacity. Its SI unit is J/kg⋅K.
Molar heat capacity quantifies the ratio of the amount of heat added (or removed) to increase (or decrease) the...
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Related Experiment Video

Updated: Jan 15, 2026

Fabrication and Testing of Photonic Thermometers
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Fabrication and Testing of Photonic Thermometers

Published on: October 24, 2018

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Efficient quantum thermal simulation.

Chi-Fang Chen1,2, Michael Kastoryano3,4, Fernando G S L Brandão5,3

  • 1Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA. achifchen@gmail.com.

Nature
|October 15, 2025
PubMed
Summary
This summary is machine-generated.

We introduce an efficient quantum algorithm for simulating quantum systems at low temperatures. This method, inspired by classical Markov Chain Monte Carlo, offers a new tool for quantum computing and physical sciences.

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Area of Science:

  • Quantum Computing
  • Physical Sciences
  • Quantum Simulation

Background:

  • Classical computers struggle with complex quantum simulations.
  • Existing quantum algorithms excel at quantum dynamics but not low-temperature phenomena.
  • Markov Chain Monte Carlo (MCMC) methods are effective for classical thermal sampling.

Purpose of the Study:

  • To develop a general-purpose quantum algorithm for simulating low-temperature quantum phenomena.
  • To create a quantum method analogous to classical MCMC for thermal distributions.
  • To provide a model for thermalization in open quantum systems.

Main Methods:

  • Proposal of an efficient quantum algorithm for thermal simulation.
  • Algorithm designed to exhibit detailed balance, similar to MCMC.
  • Incorporation of locality principles within the quantum approach.

Main Results:

  • The developed quantum algorithm efficiently simulates low-temperature quantum phenomena.
  • The algorithm successfully mimics MCMC properties like detailed balance and locality.
  • The method serves as a foundational model for quantum thermalization.

Conclusions:

  • The new quantum algorithm offers a powerful tool for simulating low-temperature quantum systems.
  • This approach may significantly impact quantum computing and physical science applications.
  • The algorithm's MCMC-like properties suggest broad applicability in quantum science.