Optimal parametric driving in Gaussian quantum systems reduces impulse estimation variance by up to a factor of two relative to steady-state operation.
Mathematical Programming Computation , volume =
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UNVERDICTED 2representative citing papers
A GPSOL- and DERL-based adaptive controller for first-order SISO nonlinear systems derives LMI-based peak-to-peak gains to set prediction error rates that enforce user-defined output error bounds, verified in simulation and on a pneumatic rig.
citing papers explorer
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Optimal State Preparation for Impulse Estimation in Gaussian Quantum Systems
Optimal parametric driving in Gaussian quantum systems reduces impulse estimation variance by up to a factor of two relative to steady-state operation.
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Online Learning-Based Control with Guaranteed Error Bounds for a Class of Nonlinear Systems
A GPSOL- and DERL-based adaptive controller for first-order SISO nonlinear systems derives LMI-based peak-to-peak gains to set prediction error rates that enforce user-defined output error bounds, verified in simulation and on a pneumatic rig.