TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.17.1.014
TI  - Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient
PY  - 2024/07/16
UR  - https://scipost.org/SciPostPhys.17.1.014
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 17
IS  - 1
SP  - 014
A1  - Benestad, Jacob
AU  - Krzywda, Jan
AU  - van Nieuwenburg, Evert
AU  - Danon, Jeroen
AB  - Slow fluctuations of Overhauser fields are an important source for decoherence in spin qubits hosted in III-V semiconductor quantum dots. Focusing on the effect of the field gradient on double-dot singlet-triplet qubits, we present two adaptive Bayesian schemes to estimate the magnitude of the gradient by a series of free induction decay experiments. We concentrate on reducing the computational overhead, with a real-time implementation of the schemes in mind. We show how it is possible to achieve a significant improvement of estimation accuracy compared to more traditional estimation methods. We include an analysis of the effects of dephasing and the drift of the gradient itself.
ER  -

@Article{10.21468/SciPostPhys.17.1.014,
	title={{Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient}},
	author={Jacob Benestad and Jan A. Krzywda and Evert van Nieuwenburg and Jeroen Danon},
	journal={SciPost Phys.},
	volume={17},
	pages={014},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.17.1.014},
	url={https://scipost.org/10.21468/SciPostPhys.17.1.014},
}