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This research applies learned inference to quantum correlation measurements, significantly improving the accuracy of multi-emitter localization under photon-limited conditions. By directly inferring emitter counts and positions rather than relying on traditional fit-and-select frameworks, the method achieves up to a tenfold precision gain while requiring fewer photons for practical imaging. This advancement offers a robust pathway for real-time biological imaging and single-particle tracking in complex, densely labeled samples.
Quantum-Correlation-Based Multi-Emitter Localization in Information-Limited Regimes via Learned Inference
arXiv:2610.24457v1 Announce Type: new Abstract: Quantum correlation measurements have emerged as a powerful tool for localizing multiple emitters with subwavelength separation. Multi-emitter localization consists of two distinct tasks: identifying the emitter count $N$ and estimating their positions $\left\{x^{i}\right\}$. Existing fit-and-select frameworks fit the measured intensity and autocorrelation data using several models corresponding to different values of $N$ and then select the best-supported model. This procedure lets errors from the multi-parameter optimization of emitter positions propagate into the choice of model, degrading performance under photon-limited and high-background conditions. We propose a method that directly infers the emitter count and positions from the multimodal data via learned inference. Learned inference improves both the emitter-counting accuracy and localization precision compared to the fit-and-select method. It achieves more than tenfold improvement in precision and requires up to five times fewer photons to achieve practically meaningful localization with the largest gains observed under low photon counts, strong background, narrowly spaced emitter configurations, and spatially truncated data. This photon-efficient quantum-assisted localization framework offers a promising route toward real-time biological imaging and single-particle tracking in densely labeled samples.
Abstract:Quantum correlation measurements have emerged as a powerful tool for localizing multiple emitters with subwavelength separation. Multi-emitter localization consists of two distinct tasks: identifying the emitter count $N$ and estimating their positions $\left\{x^{i}\right\}$. Existing fit-and-select frameworks fit the measured intensity and autocorrelation data using several models corresponding to different values of $N$ and then select the best-supported model. This procedure lets errors from the multi-parameter optimization of emitter positions propagate into the choice of model, degrading performance under photon-limited and high-background conditions. We propose a method that directly infers the emitter count and positions from the multimodal data via learned inference. Learned inference improves both the emitter-counting accuracy and localization precision compared to the fit-and-select method. It achieves more than tenfold improvement in precision and requires up to five times fewer photons to achieve practically meaningful localization with the largest gains observed under low photon counts, strong background, narrowly spaced emitter configurations, and spatially truncated data. This photon-efficient quantum-assisted localization framework offers a promising route toward real-time biological imaging and single-particle tracking in densely labeled samples.
Quantum Physics (quant-ph); Optics (physics.optics)
arXiv:2610.24457 [quant-ph]
(or arXiv:2610.24457v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.24457 Focus to learn more arXiv-issued DOI via DataCite
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