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arXiv:2604.10463

Efficient imaging of quantum emitters using compressive sensing

Sonali Gupta, Kiran Bajar, Alexander McFarland, Amit Kumar, Subhas Manna, Sushil Mujumdar

physics.opticsquant-phSubmitted Apr 12, 2026• Announced Apr 14, 2026
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Abstract

Optical imaging of quantum emitters is essential for a wide range of quantum applications. Conventional confocal imaging relies on point-by-point raster scanning, which is inherently time-consuming and photon-inefficient, particularly for sparse emitter distributions and photon-limited samples. Here, we demonstrate a compressive sensing-based imaging approach, where spatially structured wide-field excitation replaces raster scanning, enabling reconstruction of sparse emitters. In our implementation, random binary patterns are used to acquire compressive measurements, from which the spatial fluorescence distribution is reconstructed using a GPSR-BB algorithm. We experimentally demonstrate this approach using nitrogen-vacancy (NV) centers in diamond as a representative platform, with high-fidelity image reconstruction achieved using only approximately $20\%$ of the measurements required for conventional raster scanning. In addition to intensity reconstruction, we extend this framework to reconstruct spatial maps of the second-order correlation function $g^{(2)}(0)$ from compressive measurements. This enables identification of single-photon emitters through antibunching signatures using significantly reduced data.

Comments
12 pages, 6 figures

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Optical imaging of quantum emitters is essential for a wide range of quantum applications. Conventional confocal imaging relies on point-by-point raster scanning, which is inherently time-consuming and photon-inefficient, particularly for sparse emitter distributions and photon-limited samples. Here, we demonstrate a compressive sensing-based imaging approach, where spatially structured wide-field excitation replaces raster scanning, enabling reconstruction of sparse emitters. In our implementation, random binary patterns are used to acquire compressive measurements, from which the spatial fluorescence distribution is reconstructed using a GPSR-BB algorithm. We experimentally demonstrate this approach using nitrogen-vacancy (NV) centers in diamond as a representative platform, with high-fidelity image reconstruction achieved using only approximately 20%20\%20% of the measurements required for conventional raster scanning. In addition to intensity reconstruction, we extend this framework to reconstruct spatial maps of the second-order correlation function g(2)(0)g^{(2)}(0)g(2)(0) from compressive measurements. This enables identification of single-photon emitters through antibunching signatures using significantly reduced data.
Optical imaging of quantum emitters is essential for a wide range of quantum applications. Conventional confocal imaging relies on point-by-point raster scanning, which is inherently time-consuming, particularly for sparse emitter distributions and photon-limited samples. Here, we demonstrate a compressive-sensing-based fluorescence imaging approach in which spatially structured wide-field excitation is used to acquire multiplexed measurements of sparse emitters. In our implementation, random binary patterns are projected onto the sample, and the spatial fluorescence distribution is reconstructed from the measured signals using a gradient projection for a sparse reconstruction algorithm with Barzilai Borwein step size (GPSR-BB). We experimentally demonstrate this approach using nitrogen-vacancy (NV) centers in diamond as a representative platform, achieving high-fidelity image reconstruction using only approximately 20% of the measurements required for conventional raster scanning. Beyond intensity reconstruction, we develop a compressive correlation-reconstruction model for recovering spatial maps of the second-order correlation function g (2) (0) from photon-correlation measurements. Through numerical simulations, we show that the reconstructed g (2) (0) maps can identify single-photon-emitter locations using the antibunching criterion g (2) (0) < 0.5. These results demonstrate that compressive sampling can reduce measurement overhead while preserving spatial information and enabling correlation-based quantum-emitter identification.
Super-resolved reconstruction of single-photon emitter locations from g(2)(0)g^{(2)}(0)g(2)(0) maps
Single-photon sources are vital for emerging quantum technologies. In particular, Nitrogen-vacancy (NV) centers in diamond are promising due to their room-temperature stability, long spin coherence, and compatibility with nanophotonic structures. A key challenge, however, is the reliable identification of isolated NV centers, since conventional confocal microscopy is diffraction-limited and cannot resolve emitter distributions within a focal spot. Besides, the associated intensity scanning is a time-expensive procedure. Here, we introduce a raster-scanned g(2)(0)g^{(2)}(0)g(2)(0) mapping technique combined with an inversion-based reconstruction algorithm. By directly measuring local photon antibunching across the field of view, we extract the effective emitter number within each focal spot and reconstruct occupancy maps on a sub-focal-spot grid. This enables recovery of the number and spatial distribution of emitters within regions smaller than the confocal focal spot, thereby offering possibilities of going beyond the diffraction limit. Our simulations confirm robust reconstruction of NV-center distributions. The method provides a practical diagnostic tool for locating single-photon sources in an efficient and accurate manner, at much lesser time and effort compared to conventional intensity scanning. It offers valuable feedback for nanophotonic device fabrication, supporting more precise and scalable integration of NV-based quantum photonic technologies.
Single-photon sources are vital for emerging quantum technologies. In particular, Nitrogen-vacancy (NV) centers in diamond are promising due to their room-temperature stability, long spin coherence, and compatibility with nanophotonic structures. A key challenge, however, is the reliable identification of isolated NV centers, since conventional confocal microscopy is diffraction-limited and cannot resolve emitter distributions within a focal spot. Besides, the associated intensity scanning is a time-expensive procedure. Here, we introduce a simulation-based raster-scanned g ( 2 ) ( 0 ) mapping technique combined with an inversion-based reconstruction algorithm. Using synthetically generated photon-timestamp data for randomly distributed NV centers under a scanned Gaussian excitation profile, by directly measuring local photon antibunching across the field of view, we extract the effective emitter number within each focal spot and reconstruct occupancy maps on a sub-focal-spot grid. This enables recovery of the number and spatial distribution of emitters within regions smaller than the confocal focal spot, thereby offering possibilities of going beyond the diffraction limit. Simulations confirm robust reconstruction of NV-center distributions. The method reduces experimental effort in identifying isolated emitters by inferring the effective emitter number and sub-diffraction occupancy within each focal spot in a single scan, thereby avoiding unnecessary searches in regions that do not contain single emitters, as commonly encountered in intensity-based scanning approaches. It offers valuable feedback for nanophotonic device fabrication, supporting more precise and scalable integration of NV-based quantum photonic technologies.
Compressive sensing utilizes sparsity to realize efficient image reconstruction. It is a valuable processing technique when cost, power, technology or computational overhead are limited or high. In the quantum domain technology usually limits efficient acquisition of weak or fragile signals. I will discuss the basics of information theory, compression, and compressive sensing. I will then discuss our recent work in compressive sensing. The topics of discussion include low-flux laser Radar, photonic phase transitions, high resolution biphoton ghost imaging, Ghost object tracking, 3D object tracking and high dimensional entanglement characterization. I will touch lightly on our current work of rapid wavefunction reconstruction and wavefront sensing. As an example (shown below), we were able efficiently and rapidly reconstruct high dimensional joint probability functions of biphotons in momentum and position. With conventional raster scanning this process would take approximately a year, but using double-pixel compressive sensing, the pictures were acquired in a few hours with modest flux.
We implement a double-pixel compressive-sensing camera to efficiently characterize, at high resolution, the spatially entangled fields that are produced by spontaneous parametric down-conversion.This technique leverages sparsity in spatial correlations between entangled photons to improve acquisition times over raster scanning by a scaling factor up to n 2 = logðnÞ for n-dimensional images.We image at resolutions up to 1024 dimensions per detector and demonstrate a channel capacity of 8.4 bits per photon.By comparing the entangled photons' classical mutual information in conjugate bases, we violate an entropic Einstein-Podolsky-Rosen separability criterion for all measured resolutions.More broadly, our result indicates that compressive sensing can be especially effective for higher-order measurements on correlated systems.