Advancing quantum imaging through learning theory

• URL: http://arxiv.org/abs/2501.15685v1
• Date: Sun, 26 Jan 2025 22:02:13 GMT
• Title: Advancing quantum imaging through learning theory
• Authors: Yunkai Wang, Changhun Oh, Junyu Liu, Liang Jiang, Sisi Zhou,
• Abstract summary: We quantify performance of quantum imaging by modeling it as a learning task and calculating the Resolvable Expressive Capacity (REC)<n>We first examine imaging performance for two-point sources and generally distributed sources, referred to as compact sources.
• Score: 7.19995826332098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We quantify performance of quantum imaging by modeling it as a learning task and calculating the Resolvable Expressive Capacity (REC). Compared to the traditionally applied Fisher information matrix approach, REC provides a single-parameter interpretation of overall imaging quality for specific measurements that applies in the regime of finite samples. We first examine imaging performance for two-point sources and generally distributed sources, referred to as compact sources, both of which have intensity distributions confined within the Rayleigh limit of the imaging system. Our findings indicate that REC increases stepwise as the sample number reaches certain thresholds, which are dependent on the source's size. Notably, these thresholds differ between direct imaging and superresolution measurements (e.g., spatial-mode demultiplexing (SPADE) measurement in the case of Gaussian point spread functions (PSF)). REC also enables the extension of our analysis to more general scenarios involving multiple compact sources, beyond the previously studied scenarios. For closely spaced compact sources with Gaussian PSFs, our newly introduced orthogonalized SPADE method outperforms the naively separate SPADE method, as quantified by REC.

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