Hamiltonian Learning on Superconducting Qubits using Bayesian Inference

Bayesian inference uses Bayes’ theorem to update the probability of a hypothesis and as a result can be used to great effect when trying to learn the Hamiltonian of a quantum system. In comparison to traditional techniques for characterisation it has the benefit of providing statistically relevant information about the learning procedure, enabling more efficient data taking and revealing limits of the model provided to produce the data. It can be used to compare how well different models fit measured data and hence diagnose noise sources. We demonstrate this by applying it to a superconductor semiconductor 'gatemon' qubit and use it to learn the parameters of the Hamiltonian.