Magnetic-Field Learning Using a Single Electronic Spin in Diamond with One-Photon Readout at Room Temperature
At a Glance
Section titled āAt a Glanceā| Metadata | Details |
|---|---|
| Publication Date | 2019-04-29 |
| Journal | Physical Review X |
| Authors | R. Santagati, A.āA. Gentile, S Knauer, S. Schmitt, S Paesani |
| Institutions | Bristol Robotics Laboratory, Microsoft (United States) |
| Citations | 47 |
Abstract
Section titled āAbstractāNitrogen-vacancy (NV) centres in diamond are appealing nano-scale quantum\nsensors for temperature, strain, electric fields and, most notably, for\nmagnetic fields. However, the cryogenic temperatures required for low-noise\nsingle-shot readout that have enabled the most sensitive NV-magnetometry\nreported to date, are impractical for key applications, e.g. biological\nsensing. Overcoming the noisy readout at room-temperature has until now\ndemanded repeated collection of fluorescent photons, which increases the\ntime-cost of the procedure thus reducing its sensitivity. Here we show how\nmachine learning can process the noisy readout of a single NV centre at\nroom-temperature, requiring on average only one photon per algorithm step, to\nsense magnetic field strength with a precision comparable to those reported for\ncryogenic experiments. Analysing large data sets from NV centres in bulk\ndiamond, we report absolute sensitivities of $60$ nT s$^{1/2}$ including\ninitialisation, readout, and computational overheads. We show that dephasing\ntimes can be simultaneously estimated, and that time-dependent fields can be\ndynamically tracked at room temperature. Our results dramatically increase the\npracticality of early-term single spin sensors.\n
Tech Support
Section titled āTech SupportāOriginal Source
Section titled āOriginal SourceāReferences
Section titled āReferencesā- 2012 - Machine Learning: A Probabilistic Perspective