Optimal sensing on an asymmetric exceptional surface

• URL: http://arxiv.org/abs/2503.09064v2
• Date: Fri, 11 Apr 2025 01:05:16 GMT
• Title: Optimal sensing on an asymmetric exceptional surface
• Authors: Robert L. Cook, Liwen Ko, K. Birgitta Whaley,
• Abstract summary: We study the connection between exceptional points (EPs) and optimal parameter estimation.<n>Partially reflecting the output of one mode into the other creates a non-Hermitian Hamiltonian which exhibits an exceptional surface (ES)<n>We find that the QFI is enhanced in the presence of an EP for both of these input states and that both states can saturate the Cram'er-Rao bound.
• Score: 0.1755623101161125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the connection between exceptional points (EPs) and optimal parameter estimation, in a simple system consisting of two counter-propagating traveling wave modes in a microring resonator. The unknown parameter to be estimated is the strength of a perturbing cross-coupling between the two modes. Partially reflecting the output of one mode into the other creates a non-Hermitian Hamiltonian which exhibits a family of EPs, creating an exceptional surface (ES). We use a fully quantum treatment of field inputs and noise sources to obtain a quantitative bound on the estimation error by calculating the quantum Fisher information (QFI) in the output fields, whose inverse gives the Cram\'er-Rao lower bound on the mean-squared-error of any unbiased estimator. We determine the bounds for two input states, namely, a semiclassical coherent state and a highly nonclassical NOON state. We find that the QFI is enhanced in the presence of an EP for both of these input states and that both states can saturate the Cram\'er-Rao bound. We then identify idealized yet experimentally feasible measurements that achieve the minimum bound for these two input states. We also investigate how the QFI changes for parameter values that do not lie on the ES, finding that these can have a larger QFI, suggesting alternative routes to optimize the parameter estimation for this problem.

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