Knowledge-dependent optimal Gaussian strategies for phase estimation

• URL: http://arxiv.org/abs/2412.16023v2
• Date: Thu, 23 Jan 2025 08:20:42 GMT
• Title: Knowledge-dependent optimal Gaussian strategies for phase estimation
• Authors: Ricard Ravell Rodríguez, Simon Morelli,
• Abstract summary: We identify the optimal pure single-mode Gaussian probe states depending on the knowledge of the estimated phase parameter. We find that for a large prior uncertainty, the optimal probe states are close to coherent states. Surprisingly, there is a clear jump, where the optimal probe state changes abruptly to a squeezed vacuum state.
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• Abstract: When estimating an unknown phase rotation of a continuous-variable system with homodyne detection, the optimal probe state strongly depends on the value of the estimated parameter. In this article, we identify the optimal pure single-mode Gaussian probe states depending on the knowledge of the estimated phase parameter before the measurement. We find that for a large prior uncertainty, the optimal probe states are close to coherent states, a result in line with findings from noisy parameter estimation. But with increasingly precise estimates of the parameter it becomes beneficial to put more of the available energy into the squeezing of the probe state. Surprisingly, there is a clear jump, where the optimal probe state changes abruptly to a squeezed vacuum state, which maximizes the Fisher information for this estimation task. We use our results to study repeated measurements and compare different methods to adapt the probe state based on the changing knowledge of the parameter according to the previous findings.

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