GQSPI recasts binary hypothesis testing on Gaussian bosonic signals as a polynomial approximation problem, achieving O(1/d log d) decision error for circuit depth d and robustness to dephasing noise.
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This document outlines the content of a teaching module covering theoretical and experimental aspects of applied optics with emphasis on imaging, lasers, and communication technologies.
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Robust Quantum Algorithmic Binary Decision-Making on Gaussian Signals
GQSPI recasts binary hypothesis testing on Gaussian bosonic signals as a polynomial approximation problem, achieving O(1/d log d) decision error for circuit depth d and robustness to dephasing noise.