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Consider the adiabatic compression of an ideal gas in the cylinder of an automobile diesel engine. The gasoline vapor is injected into the cylinder of an automobile engine when the piston is in its expanded position. The temperature, pressure, and volume of the resulting gas-air mixture are 20 °C, 1.00 x 105 N/m2, and 240 cm3 , respectively. The mixture is then compressed adiabatically to a volume of 40 cm3. Note that, in the actual operation of an automobile engine, the compression is not...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Approaching the adiabatic timescale with machine learning.

Bryce M Henson1, Dong K Shin2, Kieran F Thomas3

  • 1Laser Physics Centre, Research School of Physics and Engineering, Australian National University, Canberra, ACT 2601, Australia; andrew.truscott@anu.edu.au Bryce.Henson@anu.edu.au.

Proceedings of the National Academy of Sciences of the United States of America
|December 12, 2018
PubMed
Summary
This summary is machine-generated.

Machine learning achieved faster-than-adiabatic decompression of Bose-Einstein condensates (BECs), setting a new speed record. This quantum control method bypasses complex theoretical modeling for rapid state preparation.

Keywords:
Bose–Einstein condensatesmachine learningoptimal quantum controlquantum transportshortcuts to adiabaticity

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Area of Science:

  • Quantum physics and atomic, molecular, and optical (AMO) physics.
  • Utilizes machine learning for quantum system control.

Background:

  • Controlling quantum systems without excitation is difficult due to modeling complexities and experimental fragility.
  • Faster-than-adiabatic protocols for Bose-Einstein condensates (BECs) are theoretically developed but experimentally challenging.
  • Existing experimental implementations have not achieved speeds beyond the adiabatic timescale.

Purpose of the Study:

  • To experimentally demonstrate a machine-learning-based approach for faster-than-adiabatic quantum system control.
  • To achieve rapid decompression and transport of a metastable helium condensate.
  • To overcome limitations of traditional theoretical modeling in experimental quantum control.

Main Methods:

  • Employed a machine-learning algorithm to control the coupled decompression and transport of a metastable helium condensate.
  • Performance was evaluated by measuring excitations in the resulting BEC after each experimental iteration.
  • The algorithm iteratively adjusted its internal system model to optimize control outputs.

Main Results:

  • The machine-learning algorithm successfully converged to a control solution for fast decompression.
  • Achieved a new experimental speed record for BEC decompression relative to the adiabatic timescale.
  • Outperformed previous experimental realizations based on theoretical approaches.

Conclusions:

  • The developed machine-learning method offers a feasible approach for fast-state preparation and transformations in quantum systems.
  • This technique does not require a pre-existing theoretical model of the system.
  • Potential implications for fundamental physics research and advanced cooling techniques are discussed.