## Marikunte, Sadhana: Comparing Measurement Accuracy of Nitrogen Vacancy Center Readouts

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#### Project Summary

Over the summer, I did research in the Quantum Engineering Lab (QEL) in the Electrical and Systems Engineering department. I worked with Dr. Lee Bassett and one of his doctoral students, David Hopper. The QEL does research in the emerging field of quantum devices, leading to advances in quantum computing, encryption, and nanoscale sensors. An important area of study is the Nitrogen-Vacancy (NV) center in diamond. An NV center occurs when a Nitrogen atom is located in the atomic structure of diamond creating a dangling bond in an adjacent location. An NV center exhibits many interesting behaviors and can be negatively charged, known as the bright state, or neutral, known as the dark state.

Currently, determining the state of an NV center is inefficient. There are different measurement methods that perform better in specific scenarios. My project focused on figuring out the best measurement method depending on external parameters using numerical simulations on MATLAB. Two signal processing methods were compared: photon summation and thresholding. The respective signal-to-noise ratios (SNR) were compared afterward. The calculation of the SNR through photon summation is pretty straightforward as it only requires easily measurable numbers: the bright state mean, the dark state mean, and the contrast. The calculation of the SNR through thresholding is a little more complicated. To use thresholding the probabilities of the NV center states have to be known. The probabilities of the state of an NV center can be represented by Poisson distributions, one for the bright state and one for the dark state. Depending on how far the means of the dark state and bright state are from one another the two Poisson distributions overlap. The intersection point, known as the threshold, of the two Poisson distributions needs to be found in order to find the false positive rate and the false negative rate needed for the SNR calculation. I then compared the two methods of calculation for a variety of parameters and analyzed the results.

Before joining the lab, I had very little experience at what nanotechnology and quantum engineering consisted of. I learned a lot about the chemistry and physics of atoms and how they can be used in computing in the future. In addition, coming from a mainly programming background I learned a lot about how to apply programming techniques to signal processing and statistics. In addition, I found how useful programming and MATLAB was for storing and computing large quantities of data. Each simulation I ran used 4,200,000 numbers. Overall, my project taught me about how the concepts I had learned in class could be applied in a real world setting.