Authors: Shah Saad Alam, Victor E. Colussi, John Drew Wilson, Jarrod T. Reilly, Michael A. Perlin, Murray J. Holland
Published on: May 13, 2024
Impact Score: 7.6
Arxiv code: Arxiv:2405.07907
Summary
- What is new: New approach using multiparameter estimation theory combined with machine learning to improve quantum sensor performance by managing noise.
- Why this is important: Noise in quantum sensors particularly when it correlates with the target signal due to fluctuating operational parameters.
- What the research proposes: A machine learning-based method to decorrelate target signals from nuisance parameters in quantum sensors using multiparameter estimation theory.
- Results: Successfully applied the method to an optical lattice-based accelerometer, enhancing its sensitivity and reducing the impact of lattice depth noise.
Technical Details
Technological frameworks used: Multiparameter estimation theory, Bayesian inferencing
Models used: Machine learning models for protocol optimization
Data used: Statistical analysis data from sensor readings and noise measurements
Potential Impact
Quantum technology providers, enterprises in precision measurement, aerospace and defense companies
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