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Dr. Daniel Soh received his first PhD in high power fiber lasers from University of Southampton, UK, in 2005. He developed and commercialized high power lasers in silicon valley companies including JDSU until he joined Sandia National Laboratories in 2009. Initially, Dr. Soh contributed to Sandia’s high power laser development. In 2013, he turned his interest to quantum information science. He went back to college for reeducation and obtained his second PhD on quantum dynamical systems from Stanford University in 2019. His recent interests are quantum communications, quantum computing, quantum sensing, and quantum networks. He has published more than 70 peer-reviewed journal articles and has authored 10 granted US patents.
Kevin Young is a staff scientist at Sandia National Laboratories with broad expertise in physical implementations of quantum computing. Kevin is the co-director of Sandia’s Quantum Performance Laboratory, a multidisciplinary research and development group within Sandia National Laboratories that develops and deploys cutting-edge techniques for assessing and improving the performance of quantum computing hardware.
Kevin earned both a BS in Physics and Mathematics and a BA in Chemistry at the College of Charleston in South Carolina. He received his PhD in Physics from the University of California, Berkeley, where he specialized in robust quantum optimal control theory and modeling of semiconducting qubit platforms. At Sandia his work focuses on identifying and mitigating errors in real quantum hardware, modeling low-level device physics of trapped-ion quantum computers, and participating in a number of standards making and advising organizations. He actively collaborates with experimental quantum computing groups across the globe.
Kevin is the recipient of the Department of Energy’s Early Career Award, a prestigious award granted to further the individual research programs of outstanding scientists with demonstrated successful research activities and potential for solving important problems to the US government. His research under this award focuses on developing fast and efficient calibration methods for quantum computers that work for all qubit technologies and can operate efficiently at scale.
Andy Mounce has research experience in condensed matter physics, semiconductor qubits, nitrogen vacancy magnetometry, and defects in wide band gap semiconductors. His expertise includes utilizing quantum information science techniques for understanding basic properties of quantum materials and quantum information relevant materials, such as superconductors, strongly correlated electronic materials, magnetic materials, and topological phases in materials. These techniques include cryogenic amplification, optically detected magnetic resonance, nitrogen vacancy detected magnetometry, photoluminescence, and bulk spin-resonance. Additionally, he is using machine learning in image analysis techniques, such as compressive sensing and neural networks, to both optimize experimental implementations and analysis.