arxiv: 2604.10463 · v1 · submitted 2026-04-12 · ⚛️ physics.optics · quant-ph

Recognition: unknown

Efficient imaging of quantum emitters using compressive sensing

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

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:28 UTC · model grok-4.3

classification ⚛️ physics.optics quant-ph

keywords compressive sensingquantum emittersNV centersfluorescence imagingsecond-order correlationg^{(2)}(0)sparse reconstructionantibunching

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The pith

Compressive sensing reconstructs images of sparse quantum emitters and their correlation maps using only 20% of conventional measurements.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes that random binary illumination patterns can replace point-by-point raster scanning in confocal setups, allowing accurate recovery of emitter positions and intensities from far fewer data points. The approach is demonstrated on nitrogen-vacancy centers in diamond, where faithful fluorescence images are obtained with roughly one-fifth the usual measurements. The same compressive data set is further used to reconstruct spatial maps of the second-order correlation function g^{(2)}(0), which distinguishes single-photon emitters by their antibunching signatures. This directly reduces acquisition time and photon waste for sparse, photon-limited samples that conventional methods handle inefficiently.

Core claim

The paper claims that a compressive sensing framework, in which random binary patterns provide wide-field excitation and the GPSR-BB algorithm reconstructs the image from the resulting measurements, yields high-fidelity spatial maps of both fluorescence intensity and g^{(2)}(0) for sparse quantum emitters, achieving this with approximately 20% of the data points required by raster scanning.

What carries the argument

The GPSR-BB algorithm, which performs sparse reconstruction from compressive measurements acquired through random binary illumination patterns.

If this is right

High-fidelity intensity images are obtained with only about 20% of the measurements used in raster scanning.
Spatial maps of g^{(2)}(0) can be reconstructed from the same compressive data to identify single-photon emitters via antibunching.
The method is suited to photon-limited samples and sparse emitter distributions.
Overall acquisition time and total photons collected are substantially lower than in conventional confocal imaging.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The framework could be tested on other sparse quantum systems such as quantum dots or trapped ions to check generality beyond NV centers.
Optimizing the choice of binary patterns instead of using purely random ones might allow even fewer measurements while preserving reconstruction quality.
Real-time versions of the reconstruction could support adaptive scanning that concentrates measurements on regions of interest.

Load-bearing premise

The emitters must be sparse enough and the random binary patterns must be sufficiently incoherent that the GPSR-BB algorithm recovers the true spatial distribution and g^{(2)}(0) values without significant artifacts.

What would settle it

Acquire both a full raster scan and a 20%-measurement compressive data set on the same sample of NV centers, then compare the reconstructed intensity image and g^{(2)}(0) map; large discrepancies in emitter positions, brightness, or correlation values would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.10463 by Alexander McFarland, Amit Kumar, Kiran Bajar, Sonali Gupta, Subhas Manna, Sushil Mujumdar.

**Figure 2.** Figure 2: (a) Schematic of the experimental setup for compressive sensing imaging using [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Figure 3: Experimental comparison of raster scanning and compressive sensing recon [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

**Figure 4.** Figure 4: Correlation coefficient and PSNR versus sampling ratio, demonstrating improved [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Modified version of the setup shown in Fig. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 6.** Figure 6: Reconstruction of the spatial 𝑔 (2) (0) map. (a) Direct measurement of 𝑔 (2) (0) at each raster-scanned position. The star markers indicate locations where 𝑔 (2) (0) < 0.5, identifying single-photon emitters. (b) Reconstruction of the 𝑔 (2) (0) map using compressive sensing with only 20% of the measurements. The circles indicate positions where the reconstructed 𝑔 (2) (0) value is below 0.5, demonstrating … view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Compressive sensing cuts NV imaging to 20% measurements in a practical way, but the spatial g(2)(0) map claim runs into a basic forward-model problem with bucket detection.

read the letter

The paper takes standard compressive sensing with random binary patterns and applies it to wide-field imaging of NV centers in diamond. They reconstruct the intensity map from roughly 20% of the usual raster-scan measurements and report that the GPSR-BB solver works on real data. They also try to push the same framework to produce spatial maps of g(2)(0) so single-photon emitters can be flagged without full scanning. That is the concrete new piece: an experimental demonstration on a quantum-emitter platform rather than another simulation study.

Referee Report

1 major / 2 minor

Summary. The manuscript demonstrates a compressive-sensing approach to imaging sparse quantum emitters (NV centers in diamond) by replacing raster scanning with random binary wide-field illumination patterns. Measurements are inverted with the GPSR-BB algorithm to recover the spatial intensity distribution using only ~20% of conventional measurements. The work further claims to reconstruct spatial maps of g^{(2)}(0) from the same compressive data set, enabling identification of single-photon emitters via antibunching signatures.

Significance. If the central claims hold, the method offers a practical route to faster, photon-efficient imaging of sparse quantum emitters, which is relevant for quantum sensing and information applications. The experimental implementation on NV centers applies established CS mathematics to a real platform and is a clear strength. The extension to g^{(2)}(0) maps, if valid, would add substantial utility by allowing single-emitter characterization with reduced acquisition time.

major comments (1)

[Abstract] Abstract (and the section describing the g^{(2)}(0) extension): the claim that spatial maps of g^{(2)}(0) can be reconstructed from compressive bucket-detector measurements lacks a compatible linear forward model. Intensity follows the linear relation y_k = sum_r p_k(r) I(r) that GPSR-BB can invert under sparsity; however, g^{(2)}(0,r) is quadratic and, with a single-element detector, yields only a spatially integrated autocorrelation. No position-resolved coincidence data or alternative hardware (e.g., SPAD array) is indicated that would permit inversion to a per-emitter g map. This directly undermines the extended central claim.

minor comments (2)

[Abstract] Abstract: the statement of 'high-fidelity' reconstruction at ~20% measurements is not accompanied by any quantitative fidelity metric (MSE, SSIM, etc.), error bars, or control comparisons, making the performance claim difficult to assess.
The manuscript should include an explicit equation for the g^{(2)} measurement model (analogous to the intensity forward model) and any additional assumptions required for the reconstruction.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for highlighting this important technical point regarding the g^{(2)}(0) claim. We address the concern directly below.

read point-by-point responses

Referee: [Abstract] Abstract (and the section describing the g^{(2)}(0) extension): the claim that spatial maps of g^{(2)}(0) can be reconstructed from compressive bucket-detector measurements lacks a compatible linear forward model. Intensity follows the linear relation y_k = sum_r p_k(r) I(r) that GPSR-BB can invert under sparsity; however, g^{(2)}(0,r) is quadratic and, with a single-element detector, yields only a spatially integrated autocorrelation. No position-resolved coincidence data or alternative hardware (e.g., SPAD array) is indicated that would permit inversion to a per-emitter g map. This directly undermines the extended central claim.

Authors: We agree with the referee's assessment. The intensity imaging uses the linear forward model y_k = sum_r p_k(r) I(r), which is inverted under sparsity with GPSR-BB. However, g^{(2)}(0) is a second-order statistic; a single-element bucket detector records only the total coincidence rate integrated over all emitters, without spatial resolution. Our experimental description does not include position-resolved coincidence hardware or a linear model that would allow compressive inversion to a per-position g^{(2)}(0,r) map. The claim in the abstract and any corresponding section therefore cannot be supported. We will revise the manuscript by removing the g^{(2)}(0) extension from the abstract and main text, limiting the work to compressive reconstruction of the fluorescence intensity distribution. This is a major revision. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper applies standard compressive sensing with random binary patterns and the GPSR-BB algorithm to reconstruct sparse NV-center intensity distributions from bucket-detector measurements, achieving the reported 20% measurement reduction via established CS theory rather than any fitted parameter or self-referential definition. The extension to spatial g^{(2)}(0) maps is presented as a direct framework extension without deriving the quadratic statistic from the linear intensity model by construction or invoking self-citations as uniqueness theorems. All load-bearing steps rely on external CS mathematics and experimental data, remaining self-contained against benchmarks with no reduction of claims to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The claim rests on the domain assumption that emitter distributions are sparse and that random binary patterns plus GPSR-BB suffice for faithful recovery; both are imported from the compressive-sensing literature rather than derived here.

axioms (2)

domain assumption Emitters are sufficiently sparse for compressive sensing to apply
Required for reconstruction from 20% measurements to succeed without artifacts.
standard math GPSR-BB algorithm accurately recovers the spatial distribution and g(2)(0) from random binary compressive measurements
Standard result in compressive sensing; invoked without re-derivation.

pith-pipeline@v0.9.0 · 5479 in / 1263 out tokens · 52216 ms · 2026-05-10T16:28:54.427168+00:00 · methodology

discussion (0)

Reference graph

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