Learning the dynamics of quantum systems using Statistical Inference

Statistical inference algorithms have found a wide range of applications in quantum technologies thanks to their noise-resilience properties and flexibility. In this talk, I will present some of the most recent research, carried out at Bristol's Quantum engineering and technology labs (QETLabs), on the characterisation and optimisation of quantum technologies using Bayesian inference. Bayesian inference protocols, such as Classical and Quantum likelihood estimation (respectively CLE and QLE) [1], have been experimentally applied to the characterisation of quantum systems [2] and the efficient estimation of magnetic fields using single spin quantum sensors [3]. Starting from these two demonstrations, we will explore new applications, considering those cases where prior knowledge of the model describing the system under study is limited [4, 5].

[1] Wiebe et al. Hamiltonian Learning and Certification Using Quantum Resources. Phys. Rev. Lett. 112, (2014)
[2] Wang et al. Experimental quantum Hamiltonian learning - Nature Physics 1, 149 (2017)
[3] Santagati et al. Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature - Phys. Rev. X (2019)
[4] Gentile et al. Characterising open quantum systems with Bayesian inference - manuscript in preparation (2019) [5] Flynn et al. Exploring acyclic graphs for the study of quantum systems – manuscript in preparation (2019)
[5] Flynn et al. Exploring acyclic graphs for the study of quantum systems – manuscript in preparation (2019)

R. Santagati, A.A. Gentile, B. Flynn, S. Paesani, N. Wiebe, C. Granade, S. Knauer, J. Wang, S. Schmidt, L.P. McGuinness, J. Rarity, F. Jelezko, A. Laing