Hamiltonian Learning with Online Bayesian Experiment Design in Practice

Understand

Estimating parameters of quantum systems is usually done by performing a sequence of predetermined experiments and post-processing the resulting data.

• It is known that online design, where the choice of the next experiment is based on the most up-to-date knowledge about the system, can offer speedups to parameter estimation.
• We apply online Bayesian experiment design to a Nitrogen Vacancy (NV) in diamond to learn the values of a five-parameter model describing its Hamiltonian and decoherence process.
• Comparing this to standard pre-determined experiment sweeps, we find that we can achieve median posterior variances on some parameters that are between 10 and 100 times better given the same amount of data.