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Thermal expansion and Thermal stress: Problem Solving01:27

Thermal expansion and Thermal stress: Problem Solving

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San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
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Thermal Sigmatropic Reactions: Overview01:16

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Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in 1,5-hexadiene, referred...
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Thermal Stress01:09

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If the temperature of an object is changed while it is prevented from expanding or contracting, the object is subjected to stress. The stress is compressive if the object expands in the absence of constraint and tensile if it contracts. This stress resulting from temperature change is known as thermal stress. It can be quite large and can cause damage. To avoid this stress, engineers may design components so they can expand and contract freely. For instance, on highways, gaps are deliberately...
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Temperature and Thermal Equilibrium01:11

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Heat and temperature are essential concepts for everyone every day. The study of heat and temperature is part of an area of physics known as thermodynamics. It is not always easy to distinguish heat and temperature.
The concept of temperature has evolved from the common concepts of hot and cold. The scientific definition of temperature explains more than just our sense of hot and cold. Temperature is operationally defined as the quantity measured with a thermometer. Furthermore, temperature is...
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Thermal Strain01:19

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Thermal strain is a concept that arises when we consider how temperature changes affect structures. Unlike the conventional assumption that structures remain constant under load, real-world scenarios often involve temperature fluctuations that can significantly impact these structures. Consider a homogeneous rod with a uniform cross-section resting freely on a flat horizontal surface. If the rod's temperature increases, the rod elongates. This elongation is proportional to the temperature...
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Quantifying Heat02:46

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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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Rapid PCR Thermocycling using Microscale Thermal Convection
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Fast Computational Deep Thermalization.

Shantanav Chakraborty1, Soonwon Choi2, Soumik Ghosh3

  • 1International Institute of Information Technology Hyderabad, CQST and CSTAR, Hyderabad, Telangana 500032, India.

Physical Review Letters
|December 5, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed computational deep thermalization, creating low-entanglement quantum states that mimic randomness. These states are computationally indistinguishable from random ones, even after measurements, offering new cryptographic possibilities.

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Area of Science:

  • Quantum Information Science
  • Computational Complexity Theory
  • Quantum Cryptography

Background:

  • Deep thermalization describes quantum systems generating randomness via partial measurements, typically requiring high complexity and entanglement.
  • Existing models often associate thermalization with highly unstructured, complex quantum states.

Purpose of the Study:

  • Introduce and demonstrate computational deep thermalization.
  • Construct efficient quantum dynamics that exhibit deep thermalization.
  • Explore the cryptographic properties of states generated by this process.

Main Methods:

  • Developing novel circuit dynamics with polylogarithmic depth.
  • Utilizing partial projective measurements on quantum states.
  • Analyzing state properties for computational indistinguishability and cryptographic security.

Main Results:

  • Created quantum states with low entanglement and polylogarithmic depth.
  • Demonstrated that these states are computationally indistinguishable from Haar random states.
  • Showcased that these pseudorandom and pseudoentangled states retain properties under local measurements.

Conclusions:

  • Computational deep thermalization offers a new paradigm where thermal-like behavior emerges from structured quantum states.
  • The low resource complexity facilitates scalable simulations of deep thermalization on quantum computers.
  • This work expands the study of computational quantum pseudorandomness beyond standard complexity classes.