Abstract Photon statistics of an optical field can be used for quantum optical sensing in low light level scenarios free of bulky optical components. However, photon-number-resolving detection to unravel the photon statistics is challenging. Here, we propose a novel detection approach, that we call ‘photon discerning’, which uses adaptive photon thresholding for photon statistical estimation without recording exact photon numbers. Our photon discerner is motivated by the field of neural networks where tunable thresholds have proven efficient for information extraction in machine learning tasks. The photon discerner maximizes Fisher information per photon by iteratively choosing the optimal threshold in real-time to approach the shot noise limit. Our proposed scheme of adaptive photon thresholding leads to unique remote-sensing applications of quantum degree of linear polarization camera and quantum LiDAR. We investigate optimal thresholds and show that the optimal photon threshold can be counter-intuitive (not equal to 1) even for weak signals (mean photon number much less than 1), due to the photon bunching effect. We also put forth a superconducting nanowire realization of the photon discerner which can be experimentally implemented in the near-term. We show that the adaptivity of our photon discerner enables it to beat realistic photon-number-resolving detectors with limited photon-number resolution in certain applications. Our work suggests a new class of detectors for information-theory driven, compact, and learning-based quantum optical sensing.
Abstract Photon statistics of an optical field can be used for quantum optical sensing in low light level scenarios free of bulky optical components. However, photon-number-resolving detection to unravel the photon statistics is challenging. Here, we propose a novel detection approach, that we call ‘photon discerning’, which uses adaptive photon thresholding for photon statistical estimation without recording exact photon numbers. Our photon discerner is motivated by the field of neural networks where tunable thresholds have proven efficient for information extraction in machine learning tasks. The photon discerner maximizes Fisher information per photon by iteratively choosing the optimal threshold in real-time to approach the shot noise limit. Our proposed scheme of adaptive photon thresholding leads to unique remote-sensing applications of quantum degree of linear polarization camera and quantum LiDAR. We investigate optimal thresholds and show that the optimal photon threshold can be counter-intuitive (not equal to 1) even for weak signals (mean photon number much less than 1), due to the photon bunching effect. We also put forth a superconducting nanowire realization of the photon discerner which can be experimentally implemented in the near-term. We show that the adaptivity of our photon discerner enables it to beat realistic photon-number-resolving detectors with limited photon-number resolution in certain applications. Our work suggests a new class of detectors for information-theory driven, compact, and learning-based quantum optical sensing.
Quantum-inspired imaging techniques have been proven to be effective for LiDAR with the advances of single photon detectors and computational algorithms. However, due to the disturbance of background noise and the varies of signal in outdoor environment, the performance of LiDAR is still far from its ultimate limit set by the quantum fluctuations of coherent probe light. In this work, we propose and demonstrate a LiDAR from the detection perspective for approaching the standard quantum-limited performance. The photon numbers of echo signals are recorded by a photon-number-resolving detector and applied to overcome heavy background noise through an active photon number filter in the LiDAR. It can approach the standard quantum limit in intensity estimation in a wide photon-flux range, and achieve a Fisher information of only 0.04 dB less than the quantum Fisher information when the mean signal photon number is 10. Experimentally, a noise-free target reconstruction and imaging is demonstrated in the daytime by the proposed LiDAR. It also performs better in reflectivity resolution when taking only 1/1000 of the measurements based on on/off detection. This work provides a fundamental strategy for constructing a LiDAR to quickly extract targets and identify materials in complex environments, which is important for intelligent agents such as autonomous vehicles.
Characterization of the photon statistics of an optical field is central to identifying light sources in various quantum-enabled technologies, from super-resolved quantum imaging to precise quantum sensing and spectroscopy. In practice, however, estimating photon-correlation functions with sufficient accuracy requires long acquisition times and computationally intensive statistical estimators, severely limiting real-time operation and the study of rapidly varying or weak sources. Here, we show that learned inference based on artificial neural networks enables rapid estimation of second-order coherence directly from photon-number-resolved measurements, outperforming the precision of conventional statistical methods with processing times three orders of magnitude shorter. Our approach is source-agnostic and remains accurate in few-photon illumination regimes where the statistics of coherent, partially coherent, thermal, and super-thermal light become nearly indistinguishable. We experimentally validate the method across more than 4000 distinct classical light sources, demonstrating equal or improved precision with drastically reduced data and computational requirements. These results establish machine-learning-enabled photon-statistics inference as a new operational route to rapid optical-coherence diagnostics, enabling real-time monitoring of classical light sources in regimes previously inaccessible to conventional estimators.
We present a technique that improves the signal-to-noise-ratio (SNR) of range-finding, sensing, and other light-detection applications. The technique filters out low photon numbers using photon-number-resolving detectors. This technique has no classical analog and cannot be done with classical detectors. We investigate the properties of our technique and show under what conditions the scheme surpasses the classical SNR. Finally, we simulate the operation of a rangefinder, showing improvement with a low number of signal samplings and confirming the theory with a high number of signal samplings.
Abstract Photon number resolving (PNR) measurements are beneficial or even necessary for many applications in quantum optics. Unfortunately, PNR detectors are usually large, slow, expensive, and difficult to operate. However, if the input signal is multiplexed, photon ‘click’ detectors, that lack an intrinsic PNR capability, can still be used to realize photon number resolution. Here, we investigate the operation of a single click detector, together with a storage line with tunable outcoupling. Using adaptive feedback to adjust the storage outcoupling rate, the dynamic range of the detector can in certain situations be extended by up to an order of magnitude relative to a purely passive setup. An adaptive approach can thus allow for photon number variance below the quantum shot noise limit under a wider range of conditions than using a passive multiplexing approach. This can enable applications in quantum enhanced metrology and quantum computing.
There has been much recent interest in quantum optical interferometry for applications to metrology, subwavelength imaging, and remote sensing such as in quantum laser radar (LADAR). For quantum LADAR, atmospheric absorption rapidly degrades any quantum state of light, so that for high-photon loss the optimal strategy is to transmit coherent states of light, which suffer no worse loss than the Beer law for classical optical attenuation, and which provides sensitivity at the shot-noise limit. We show that coherent light coupled with photon-number-resolving detectors can provide a super-resolution much below the Rayleigh diffraction limit, with sensitivity no worse than shot noise in terms of the detected photon power.
Standard linear optical detectors have a maximum sensitivity in the few hundreds of photons range, limited by amplifier noise. On the other hand, single photon detectors, which are the most sensitive detectors, are strongly nonlinear: One or more photons result in the same output signal. Photon number resolving (PNR) detectors, which have the ability to discriminate the number of photons in a weak optical pulse, are of great importance in the field of quantum information processing and quantum cryptography. Moreover, a PNR detector with large dynamic range can cover the gap between these two detection modes. Such detectors are greatly desirable not only in quantum information science and technology, but also in any application dealing with low light levels. In this work, we propose a novel approach to photon number resolving detectors based on spatial multiplexing of nanowire superconducting single-photon detectors. In the proposed approach, N superconducting nanowires, each connected in parallel to an integrated resistor, are connected in series. Photon absorption in a nanowire switches its bias current to the parallel resistor, forming a voltage pulse across it. The sum of these voltages, proportional to the number of absorbed photons, is measured at the output. The use of a cryogenic preamplifier with high input impedance for the read-out increases the linearity, the signal to noise ratio, and the speed. With this combination, we expect to be able to count up to few tens of photons with high fidelity, excellent timing resolution, and very high sensitivity in the telecommunication wavelength range.