Simplified algorithms for adaptive experiment design in parameter estimation

Abstract

In experiments to estimate parameters of a parametric model, Bayesian experiment design allows measurement settings to be chosen based on utility, which is the predicted improvement of parameter distributions due to modeled measurement results. In this paper we compare information-theory-based utility with three alternative utility algorithms. Tests of these utility alternatives in simulated adaptive measurements demonstrate large improvements in computational speed with slight impacts on measurement efficiency.

10 pages, 8 figures

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• Entanglement-free Heisenberg-limited phase estimation
• Robust Online Hamiltonian Learning
• Nanoscale magnetometry using a single spin system in diamond
• Optimal Bayesian experimental design for subsurface flow problems
• Simplified algorithms for adaptive experiment design in parameter estimation
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Cited by in corpus (4)

• Simplified algorithms for adaptive experiment design in parameter estimation
• Capturing dynamical correlations using implicit neural representations
• Real-time adaptive estimation of decoherence timescales for a single qubit
• Efficient Qubit Calibration by Binary-Search Hamiltonian Tracking