Nitrogen-vacancy centres: a model system for learning and sensing experiments

Efficient modelling and validation of a quantum system’s Hamiltonian – like trapped ions and atoms, quantum dots, 2D-materials or colour centres – is intractable to classical computers [1], in particular when these physical systems are scaled-up or reach higher complexities [2]. The electron spin of a negatively charged nitrogen-vacancy center (NV-) in diamond is a perfect model system of such a Hamiltonian. It allows optical initialisation and readout of its ground state electronic spin, which may subsequently be manipulated using microwave fields. Its long spin coherence times, even at room-temperature, allow its use for quantum information processing [3].
In this talk, I’ll give a short introduction into colour centres and then highlight two recent experiments. First, I’ll show how the electron spin dynamics of an NV- centre in bulk diamond can be used to experimentally demonstrate Quantum Hamiltonian Learning (QHL) on a programmable silicon-photonics quantum simulator [5]. Here, QHL is able to efficiently validate predictions for quantum systems given by model Hamiltonians [3]. Second, I’ll show machine learning algorithms can be applied to the single spin NV- centre to achieve a magnetic sensitivity that scales with the Heisenberg limit [6]. In this so-called Magnetic-Field Learning (MFL) protocol we demonstrate how dephasing times can be simultaneously estimated, and that time-dependent fields can be dynamically tracked at room temperature. Our results dramatically increase the practicality of single spin sensors, which used to be limited to cryogenic temperatures.

References:

1. R. P. Feynman. Simulating physics with computers. International J. of theoretical physics, 21, 467-488,1982.
2. Y. C. Chen et al. Laser writing of coherent colour centres in diamond. Nat. Photon. 236, 2016.
3. F. Jelezko et al. Observation of coherent oscillations in a single electron spin. Phys. Rev. Lett. 92, 1-4, 2004.
4. N. Wiebe et al. Hamiltonian learning and certification using quantum resources. Phys. Rev. Lett. 112, 190501, 2014.
5. J. Wang et al. Experimental quantum Hamiltonian learning. Nat. Physics., DOI: 10.1038/NPHYS4074, 2017.
6. R. Santagati*, A. A. Gentile*, S. Knauer* et al. Magnetic-field-learning using a single electronic spin in diamond with one-photon-readout at room temperature. arXiv1807.09753, 2018.