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Sharp transitions in low-number quantum dots Bayesian magnetometry.

Paweł Mazurek1,2, Michał Horodecki1,2, Łukasz Czekaj1,2

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This study explores estimating magnetic fields using quantum dot spins and environmental interactions. Entanglement aids optimal measurements, but environmental memory effects offer limited metrological advantages at low magnetic fields.

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Area of Science:

  • Quantum physics
  • Quantum information science
  • Condensed matter physics

Background:

  • Quantum dot spins are sensitive to magnetic fields.
  • Hyperfine interaction mediates spin entanglement with the environment.
  • Bayesian inference is a powerful tool for parameter estimation.

Purpose of the Study:

  • To investigate the Bayesian estimation of static magnetic fields in quantum dot spin systems.
  • To analyze the role of entanglement and environmental interactions in metrology.
  • To determine the limitations of metrological advantage due to environmental memory effects.

Main Methods:

  • Utilizing a Gaussian prior for Bayesian estimation.
  • Modeling the interaction of few electron quantum dot spins with an infinite temperature spin environment via hyperfine interaction.
  • Observing sudden transitions in optimal states and measurements.

Main Results:

  • Demonstrated the usefulness of measuring occupation levels throughout the system's evolution.
  • Highlighted the crucial role of entanglement in achieving optimal estimation scenarios.
  • Found that environmental memory effects provide limited metrological advantage for low magnetic field values.

Conclusions:

  • Quantum dot spin systems offer a platform for magnetic field estimation.
  • Entanglement is key to optimizing measurement strategies.
  • Environmental interactions, while influential, can limit the precision of magnetic field sensing at low field strengths.