State-vector geometry and guided-wave physics behind optical super-resolution.
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We examine the state-vector geometry and guided-wave physics underpinning spatial super-resolution, which can be attained in far-field linear microscopy via a combination of statistical analysis, quantum optics, and spatial mode demultiplexing. A suitably tailored guided-wave signal pickup is shown to provide an information channel that can distill the super-resolving spatial modes, thus enabling an estimation of sub-Rayleigh space intervals . We derive closed-form analytical expressions describing the distribution of the -estimation Fisher information over waveguide modes, showing that this information remains nonvanishing as 0, thus preventing the variance of estimation from diverging at 0. We demonstrate that the transverse refractive index profile nQ(r) tailored to support the optimal wave function Q(r) for super-resolving estimation encodes the same information about as the entire manifold of waveguide modes needed to represent Q(r). Unlike Q(r), nQ(r) does not need a representation in a lengthy manifold of eigenmodes and can be found instead via adaptive feedback-controlled learning.