**Nitrogen-vacancy centres as model systems for Quantum Hamiltonian Learning ** - Sebastian Knauer

Efficient modelling and validation of a quantum systemâ€™s Hamiltonian - like trapped ions and atoms, quantum dots, 2D-materials, or colour centres - is intractable to classical computers, in particular when these physical systems are scaled-up or reach higher complexities ^{[1]}. Quantum Hamiltonian learning (QHL) is efficiently able to validate predictions for quantum systems given by model Hamiltonians ^{[2]}. In this approach, the exponential speed-up in reproducing the dynamics of the quantum system is given by the combination of quantum simulation with machine learning that enables to estimate the best Hamiltonian parameters among those accessible by the simulator. Here, we show for the first time, how the electron spin dynamics of an NV- centre in bulk diamond can be used to experimentally demonstrate QHL on a programmable silicon-photonics quantum simulator ^{[3,4]}. Primarily, this talk will focus on the experimental interfacing of both systems.

References:

1. R. P. Feynman. Simulating physics with computers. International J. of theoretical physics, 21, 467-488,1982.

2. N. Wiebe et al. Hamiltonian learning and certification using quantum resources. Phys. Rev. Lett. 112, 190501, 2014.

3. J.Wang et al. Experimental quantum Hamiltonian learning. Nat. Phys. 13:6, 551-555, 2017.

4. R. Santagati et al. Quantum simulation of Hamiltonian spectra on a silicon chip. arXiv:1611.03511, 2016.