arXiv subjects

Mahmoud Kalash

Publications and source records attributed to Mahmoud Kalash.

9 recordsLinked to original sources

arXiv2025

Efficient detection of spectrally multimode squeezed light through optical parametric amplification

Multimode squeezed light is a key resource for high-dimensional photonic quantum technologies, enabling applications in quantum-enhanced sensing, quantum communication, and quantum computing. Efficient detection of such a multimode squeezed state is essential for unlocking its full potential. Optical parametric amplification (OPA) has recently gained attention as a powerful technique offering loss-tolerant, direct broadband detection, and multimode operation. While OPA has been used to characterize spatially multimode squeezing, its application to spectrally multimode squeezing has not yet been demonstrated. Here, we report on the first experimental demonstration of spectrally multimode squeezing detection using OPA. We achieve simultaneous detection of squeezing across more than 60 spectral modes of a broadband squeezed vacuum state. The observed squeezing is nearly uniform, ranging from -6.5 to -7 dB, which makes the source particularly suitable for constructing continuous-variable cluster states, and indicates the multimode capability of the OPA. The results extend the capabilities of OPA detection into the spectral domain, advancing spectral-mode-based high-dimensional photonic quantum technologies.

quant-ph↗

arXiv2025

Certifying non-classicality and non-Gaussianity through optical parametric amplification

Non-Gaussian states of light are essential for numerous quantum information protocols; thus, certifying non-Gaussianity is crucial. Full quantum state tomography, commonly used for this purpose, is a complicated procedure and yields inconclusive results for strongly mixed states. Certifying non-Gaussianity through directly measurable parameters is a simpler alternative, typically achieved by measuring photon-number probabilities - either directly, using photon-number resolving detectors, or through Hanbury Brown--Twiss type measurements with single-photon detectors. Here, we demonstrate theoretically and experimentally that optical parametric amplification combined with conventional intensity detectors can effectively replace this approach without the need for photon-number resolution. In our method, we measure the mean photon number and the second-order correlation function for the amplified state. Using it, we successfully certify the non-Gaussianity of a heralded quasi-single-photon state. Since optical parametric amplification is a broadband and multimode process, our method provides a foundation for developing high-dimensional quantum technologies utilizing broadband multimode non-Gaussian states.

quant-ph↗

arXiv2025

Real-Time Monitoring of Multimode Squeezing

Multimode squeezed light is a key resource for high-dimensional quantum technologies, enhancing metrological sensitivity, boosting communication security, and enabling parallel processing in computation. Its practical potential, however, remains constrained by the inherent single-mode operation of homodyne detection, necessitating post-processing for multimode characterization. Here, we overcome this long-standing challenge by employing multimode optical parametric amplification (MOPA), enabling loss-tolerant direct detection of squeezing in each mode, which in turn permits mode sorting after amplification. As a result, we demonstrate, for the first time to the best of our knowledge, the real-time monitoring of multimode squeezing. With a spatial light modulator sorting the modes, we simultaneously measure squeezing in nine spatial modes co-propagating within one beam. Although mode sorting and filtering reduce the detection efficiency to less than $0.3\%$, we observe high-purity squeezing of up to $-7.9 \pm 0.6$ dB -- to the best of our knowledge, the highest squeezing recorded for pulsed light. Furthermore, we demonstrate real-time, loss-tolerant characterization of continuous-variable entanglement and extend it to the detection of cluster states. Similar methods can be applied in the frequency domain, facilitating a crucial capability for scalable quantum technologies.

quant-ph↗

arXiv2024

Simultaneous measurement of multimode squeezing

Multimode squeezed light is an increasingly popular tool in photonic quantum technologies, including sensing, imaging, and computation. Meanwhile, the existing methods of its characterization are technically complicated, and in the best case, deal with a single mode at a time. Here, we demonstrate experimentally how the squeezing can be measured in multiple spatial modes simultaneously, using optical parametric amplification and direct detection followed by modal decomposition based on spatial intensity correlations. We apply this method to a multimode squeezed vacuum generated via high-gain parametric down-conversion. We measure the degrees of squeezing and anti-squeezing for eight strongest spatial modes, obtaining highest squeezing and anti-squeezing values of $-5.2 \pm 0.2$ dB and $8.6 \pm 0.3$ dB, respectively.

quant-ph↗

arXiv2022

Wigner Function Tomography via Optical Parametric Amplification

Wigner function tomography is indispensable for characterizing quantum states, but its commonly used version, balanced homodyne detection, suffers from several weaknesses. First, it requires efficient detection, which is critical for measuring fragile non-Gaussian states, especially bright ones. Second, it needs a local oscillator, tailored to match the spatiotemporal properties of the state under test, and fails for multimode and broadband states. Here we propose Wigner function tomography based on optical parametric amplification followed by direct detection. The method is immune to detection inefficiency and loss, and suitable for broadband, spatially and temporally multimode quantum states. To prove the principle, we experimentally reconstruct the Wigner function of squeezed vacuum occupying a single mode of a strongly multimode state. We obtain a squeezing of $-7.5\pm 0.4$ dB and a purity of $0.91^{+0.09}_{-0.08}$ despite more than $97\%$ loss caused mainly by filtering. Theoretically, we also consider the reconstruction of a squeezed single photon - a bright non-Gaussian state. Due to strong multimode parametric amplification, the method allows for the simultaneous tomography of multiple modes. This makes it a powerful tool for optical quantum information processing.

quant-ph↗

arXiv2020

Reconstructing 2D spatial modes for classical and quantum light

We propose a method for finding 2D spatial modes of thermal field through a direct measurement of the field intensity and an offline analysis of its spatial fluctuations. Using this method, in a simple and efficient way we reconstruct the modes of a multimode fiber and the spatial Schmidt modes of squeezed vacuum generated via high-gain parametric down conversion. The reconstructed shapes agree with the theoretical results.

quant-ph↗

arXiv2019

Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks

Salient object detection is a problem that has been considered in detail and \textcolor{black}{many solutions have been proposed}. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. Initially, we present a novel deep learning solution based on a hierarchical representation of relative saliency and stage-wise refinement. Further to this, we present data, analysis and baseline benchmark results towards addressing the problem of salient object ranking. Methods for deriving suitable ranked salient object instances are presented, along with metrics suitable to measuring algorithm performance. In addition, we show how a derived dataset can be successively refined to provide cleaned results that correlate well with pristine ground truth in its characteristics and value for training and testing models. Finally, we provide a comparison among prevailing algorithms that address salient object ranking or detection to establish initial baselines providing a basis for comparison with future efforts addressing this problem. \textcolor{black}{The source code and data are publicly available via our project page:} \textrm{\href{https://ryersonvisionlab.github.io/cocosalrank.html}{ryersonvisionlab.github.io/cocosalrank}}

cs.CV↗

arXiv2018

Semantics Meet Saliency: Exploring Domain Affinity and Models for Dual-Task Prediction

Much research has examined models for prediction of semantic labels or instances including dense pixel-wise prediction. The problem of predicting salient objects or regions of an image has also been examined in a similar light. With that said, there is an apparent relationship between these two problem domains in that the composition of a scene and associated semantic categories is certain to play into what is deemed salient. In this paper, we explore the relationship between these two problem domains. This is carried out in constructing deep neural networks that perform both predictions together albeit with different configurations for flow of conceptual information related to each distinct problem. This is accompanied by a detailed analysis of object co-occurrences that shed light on dataset bias and semantic precedence specific to individual categories.

cs.CV↗

arXiv2018

Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects

Salient object detection is a problem that has been considered in detail and many solutions proposed. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. The solution presented in this paper solves this more general problem that considers relative rank, and we propose data and metrics suitable to measuring success in a relative object saliency landscape. A novel deep learning solution is proposed based on a hierarchical representation of relative saliency and stage-wise refinement. We also show that the problem of salient object subitizing can be addressed with the same network, and our approach exceeds performance of any prior work across all metrics considered (both traditional and newly proposed).

cs.CV↗