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Optimization of a solid-state electron spin qubit using gate set tomography

New Journal of Physics

Dehollain, Juan P.; Muhonen, Juha T.; Blume-Kohout, Robin; Rudinger, Kenneth M.; Bays, Nathan R.; Nielsen, Erik N.; Laucht, Arne; Simmons, Stephanie; Kalra, Rachpon; Morello, Andrea

State of the art qubit systems are reaching the gate fidelities required for scalable quantum computation architectures. Further improvements in the fidelity of quantum gates demands characterization and benchmarking protocols that are efficient, reliable and extremely accurate. Ideally, a benchmarking protocol should also provide information on how to rectify residual errors. Gate set tomography (GST) is one such protocol designed to give detailed characterization of as-built qubits. We implemented GST on a high-fidelity electron-spin qubit confined by a single 31P atom in 28Si. The results reveal systematic errors that a randomized benchmarking analysis could measure but not identify, whereas GST indicated the need for improved calibration of the length of the control pulses. After introducing this modification, we measured a new benchmark average gate fidelity of , an improvement on the previous value of . Furthermore, GST revealed high levels of non-Markovian noise in the system, which will need to be understood and addressed when the qubit is used within a fault-tolerant quantum computation scheme.

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TYPE Journal Article YEAR 2016
DOIOSTIScopus

Using Machine Learning in Adversarial Environments

Davis, Warren L.; Dunlavy, Daniel M.; Vorobeychik, Yevgeniy; Butler, Karin; Forsythe, Chris; Letter, Matthew; Murchison, Nicole; Nauer, Kevin

Cyber defense is an asymmetric battle today. We need to understand better what options are available for providing defenders with possible advantages. Our project combines machine learning, optimization, and game theory to obscure our defensive posture from the information the adversaries are able to observe. The main conceptual contribution of this research is to separate the problem of prediction, for which machine learning is used, and the problem of computing optimal operational decisions based on such predictions, coupled with a model of adversarial response. This research includes modeling of the attacker and defender, formulation of useful optimization models for studying adversarial interactions, and user studies to measure the impact of the modeling approaches in realistic settings.

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TYPE SAND Report YEAR 2016
DOIOSTI

Standardizing Power Monitoring and Control at Exascale

Computer

Grant, Ryan; Levenhagen, Michael; Olivier, Stephen L.; Debonis, David; Bays, Nathan R.; Bays, Nathan R.

Power API - the result of collaboration among national laboratories, universities, and major vendors - provides a range of standardized power management functions, from application-level control and measurement to facility-level accounting, including real-time and historical statistics gathering. Support is already available for Intel and AMD CPUs and standalone measurement devices.

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TYPE Journal Article YEAR 2016
DOIOSTIScopus
Results 4501–4525 of 9,998
Results 4501–4525 of 9,998
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