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Photon statistics of an optical field can be used for quantum optical sensingin low light level scenarios free of bulky optical components. However,photon-number-resolving detection to unravel the photon statistics ischallenging. Here, we propose a novel detection approach, that we call `photondiscerning', which uses adaptive photon thresholding for photon statisticalestimation without recording exact photon numbers. Our photon discerner ismotivated by the field of neural networks where tunable thresholds have provenefficient for isolating optimal decision boundaries in machine learning tasks.The photon discerner maximizes Fisher information per photon by iterativelychoosing the optimal threshold in real-time to approach the shot noise limit.Our proposed scheme of adaptive photon thresholding leads to uniqueremote-sensing applications of quantum DoLP (degree of linear polarization)camera and quantum LiDAR. We investigate optimal thresholds and show that theoptimal photon threshold can be counter-intuitive (not equal to 1) even forweak signals (mean photon number much less than 1), due to the photon bunchingeffect. We also put forth a superconducting nanowire realization of the photondiscerner which can be experimentally implemented in the near-term. We showthat the adaptivity of our photon discerner enables it to beat realisticphoton-number-resolving detectors with limited photon-number resolution. Ourwork suggests a new class of detectors for information-theory driven, compact,and learning-based quantum optical sensing.

Photon discerner: Adaptive quantum optical sensing near the shot noise limit