Nitrogen-vacancy centres: a model system for learning and sensing experiments
October 25, 2018 @ 3:00 pm
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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 , in particular when these physical systems are scaled-up or reach higher complexities . 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 .
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 . Here, QHL is able to efficiently validate predictions for quantum systems given by model Hamiltonians . 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 . 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.
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