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Phase Transitions: Vaporization and Condensation02:39

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The physical form of a substance changes on changing its temperature. For example, raising the temperature of a liquid causes the liquid to vaporize (convert into vapor). The process is called vaporization—a surface phenomenon. Vaporization occurs when the thermal motion of the molecules overcome the intermolecular forces, and the molecules (at the surface) escape into the gaseous state. When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase...
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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
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Phase Transitions: Melting and Freezing02:39

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Heating a crystalline solid increases the average energy of its atoms, molecules, or ions, and the solid gets hotter. At some point, the added energy becomes large enough to partially overcome the forces holding the molecules or ions of the solid in their fixed positions, and the solid begins the process of transitioning to the liquid state or melting. At this point, the temperature of the solid stops rising, despite the continual input of heat, and it remains constant until all of the solid is...
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In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
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Unveiling quantum phase transitions from traps in variational quantum algorithms.

Chenfeng Cao1,2, Filippo Maria Gambetta1, Ashley Montanaro1,3

  • 1Phasecraft Ltd, London, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces a hybrid quantum-classical algorithm to detect quantum phase transitions. The method uses machine learning to identify critical points, improving efficiency for low-temperature physical systems.

Keywords:
Computational sciencePhase transitions and critical phenomenaQuantum informationQuantum simulation

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Area of Science:

  • Quantum physics
  • Condensed matter physics
  • Machine learning

Background:

  • Characterizing quantum phase transitions (QPTs) is crucial for understanding low-temperature physical systems.
  • Identifying ground states and order parameters are key challenges in QPT research.

Purpose of the Study:

  • To develop a hybrid quantum-classical algorithm for efficient QPT detection.
  • To leverage near-term quantum computers and machine learning for identifying critical points.

Main Methods:

  • A hybrid algorithm combining quantum optimization and classical machine learning (LASSO and Transformer models).
  • Utilizing a sliding window scan of Hamiltonian parameters to learn order parameters.
  • Validation through numerical simulations and experiments on Rigetti's Ankaa 9Q-1 quantum computer.

Main Results:

  • Successful identification of conventional and topological phase transitions.
  • Demonstrated capability to locate critical points with enhanced efficiency and precision.
  • Validation of the protocol on real quantum hardware.

Conclusions:

  • The developed hybrid protocol offers a framework for QPT investigation using shallow quantum circuits.
  • This approach integrates near-term quantum computing and machine learning for condensed matter research.
  • The method shows potential for improved efficiency and precision in studying QPTs.