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The earliest recorded discussion of the basic structure of matter comes from ancient Greek philosophers. Leucippus and Democritus argued that all matter was composed of small, finite particles that they called atomos, meaning “indivisible.” Later, Aristotle and others came to the conclusion that matter consisted of various combinations of the four “elements” — fire, earth, air, and water — and could be infinitely divided. Interestingly, these philosophers...
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The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
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Machine Learning Based Localization and Classification with Atomic Magnetometers.

Cameron Deans1, Lewis D Griffin2, Luca Marmugi1

  • 1Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom.

Physical Review Letters
|February 6, 2018
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Machine learning enhances atomic magnetometer imaging, accurately identifying object properties like position and material. This quantum-based approach improves localization and classification, applicable to diverse fields from biomedicine to security.

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Area of Science:

  • Quantum sensing
  • Machine learning applications
  • Electromagnetic induction imaging

Background:

  • Atomic magnetometers offer high sensitivity for imaging.
  • Machine learning can extract complex information from sensor data.
  • Electromagnetic induction imaging is useful for material characterization.

Purpose of the Study:

  • To apply machine learning to ^{85}Rb atomic magnetometer data for enhanced object identification.
  • To demonstrate improved localization and classification of objects.
  • To explore machine learning's utility in diffusive electromagnetic systems.

Main Methods:

  • Utilized a ^{85}Rb atomic magnetometer for electromagnetic induction imaging.
  • Applied machine learning algorithms to analyze and interpret imaging data.
  • Focused on extracting object position, material, orientation, and shape.

Main Results:

  • Achieved localization 2.6 times better than the imaging system's spatial resolution.
  • Reached up to 97% accuracy in object classification.
  • Demonstrated machine learning's ability to uncover hidden data and circumvent inverse problem solving.

Conclusions:

  • Machine learning significantly enhances information extraction from atomic magnetometer imaging.
  • This approach extends machine learning to diffusive systems like low-frequency electrodynamics.
  • Automated information retrieval from quantum-based imaging has broad implications for biomedicine and security.