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HL: Fachverband Halbleiterphysik
HL 33: Poster II
HL 33.27: Poster
Wednesday, March 29, 2023, 17:00–19:00, P1
Adaptive Bayesian estimation of an Overhauser field gradient — •Jacob Benestad1, Jan Krzywda2, Evert van Nieuwenburg2,3, Fabrizio Berritta3, Torbjørn Rasmussen3, Anasua Chatterjee3, Ferdinand Kuemmeth3, and Jeroen Danon1 — 1Center for Quantum Spintronics, Norwegian University of Science and Technology, Norway — 2Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands — 3Center for Quantum Devices, University of Copenhagen, Denmark
Slow fluctuations of the Overhauser field gradient are an important source for decoherence in singlet-triplet spin qubits hosted in type III-V semiconductors. Single-shot Ramsey experiments are well suited for Bayesian inference of the Overhauser gradient, where smart experiment design and prior knowledge can be leveraged to increase the information gain of a new measurement. This has led to the development of adaptive schemes, where between each measurement one attempts to determine the optimal next experiment in order to gain the most possible information about an Overhauser field gradient before the qubit decoheres. A real-time exact treatment of this problem at each step is difficult to achieve in an experimental setting. However an approximate treatment using only Gaussian distributions has been shown to give an exponential reduction of the distribution variance and would require only tracking two parameters. We propose a modification of this scheme that should make it more robust for gradients distributed around a mean of zero by evaluating the squared values and performing the Bayesian update scheme on the resulting chi-square distribution.