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Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm.

Moji Ghadimi1, Alexander Zappacosta2, Jordan Scarabel2

  • 1Center for Quantum Dynamics, Griffith University, Nathan, QLD, Australia. moji131@gmail.com.

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Summary
This summary is machine-generated.

Surface ion traps for quantum computing face challenges with stray electric fields. This study uses gradient descent and machine learning for automated compensation, significantly improving ion fluorescence rates.

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Area of Science:

  • Quantum Computing
  • Atomic Physics
  • Surface Science

Background:

  • Surface ion traps are crucial for scalable quantum computing but present challenges in managing stray electric fields due to complex geometries.
  • Accurate control of electric fields is essential for maintaining ion coherence and enabling complex quantum operations.

Purpose of the Study:

  • To develop and demonstrate an automated method for compensating stray electric fields in surface ion traps.
  • To evaluate the effectiveness of machine learning and gradient descent algorithms compared to manual compensation techniques.

Main Methods:

  • Implemented a gradient descent algorithm and a deep learning network for automated stray electric field compensation.
  • Tested the compensation method against electric field disturbances induced by UV laser light on the trap surface.
  • Measured the improvement in compensation by observing the fluorescence rate of trapped Ytterbium-171 ions.

Main Results:

  • Automated compensation using gradient descent improved ion fluorescence by 78%.
  • Machine learning-based compensation further enhanced fluorescence by 96% compared to manual methods.
  • The developed techniques effectively mitigated electric field fluctuations caused by UV laser-induced charging.

Conclusions:

  • Gradient descent and machine learning offer superior automated compensation for stray electric fields in surface ion traps.
  • These advanced compensation techniques are vital for improving the performance and scalability of quantum computing architectures.
  • The demonstrated method provides a robust solution for real-time electric field control in ion trap systems.