A CNN with sliding-window augmentation estimates exchange, DMI, and magnetization from experimental spin configuration images, demonstrated on FeGe and FeGe0.5Si0.5 skyrmions.
Quantum parameter estimation with a neural network
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abstract
We propose to use neural networks to estimate the rates of coherent and incoherent processes in quantum systems from continuous measurement records. In particular, we adapt an image recognition algorithm to recognize the patterns in experimental signals and link them to physical quantities. We demonstrate that the parameter estimation works unabatedly in the presence of detector imperfections which complicate or rule out Bayesian filter analyses.
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cond-mat.dis-nn 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Machine learning magnetic parameters from spin configurations
A CNN with sliding-window augmentation estimates exchange, DMI, and magnetization from experimental spin configuration images, demonstrated on FeGe and FeGe0.5Si0.5 skyrmions.