We propose a self-optimizing photonic neural network based on multiplane light conversion (MPLC) for quantum parameter estimation. Unlike static mode sorters, our system adaptively learns measurement strategies by maximizing Classical Fisher Information (CFI) employing a minimal number of detectors using a simultaneous perturbation stochastic approximation algorithm. We demonstrate this on the two-point source separation problem. Our approach achieves near-optimal precision and maintains robustness against centroid misalignment, outperforming standard direct imaging and spatial mode demultiplexing methods.
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