arXiv subjects

Sylvain Gigan

Publications and source records attributed to Sylvain Gigan.

At least 37 records · Page 2Linked to original sources

arXiv2025

Three-dimensional holographic imaging of incoherent objects through scattering media

Three-dimensional (3D) high-resolution imaging is essential in microscopy, yet light scattering poses significant challenges in achieving it. Here, we present an approach to holographic imaging of spatially incoherent objects through scattering media, utilizing a virtual medium that replicates the scattering effects of the actual medium. This medium is constructed by retrieving mutually incoherent fields from the object, and exploiting the spatial correlations between them. By numerically propagating the incoherent fields through the virtual medium, we non-invasively compensate for scattering, achieving accurate 3D reconstructions of hidden objects. Experimental validation with fluorescent and synthetic incoherent objects confirms the effectiveness of this approach, opening new possibilities for advanced 3D high-resolution microscopy in scattering environments.

physics.optics↗

arXiv2025

Roadmap on Neuromorphic Photonics

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field.

cs.ET↗

arXiv2024

Nonlinear optical encoding enabled by recurrent linear scattering

Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity-a critical component of computation-remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design's efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

physics.optics↗

arXiv2024

Optical next generation reservoir computing

Artificial neural networks with internal dynamics exhibit remarkable capability in processing information. Reservoir computing (RC) is a canonical example that features rich computing expressivity and compatibility with physical implementations for enhanced efficiency. Recently, a new RC paradigm known as next generation reservoir computing (NGRC) further improves expressivity but compromises its physical openness, posing challenges for realizations in physical systems. Here we demonstrate optical NGRC with computations performed by light scattering through disordered media. In contrast to conventional optical RC implementations, we drive our optical reservoir directly with time-delayed inputs. Much like digital NGRC that relies on polynomial features of delayed inputs, our optical reservoir also implicitly generates these polynomial features for desired functionalities. By leveraging the domain knowledge of the reservoir inputs, we show that the optical NGRC not only predicts the short-term dynamics of the low-dimensional Lorenz63 and large-scale Kuramoto-Sivashinsky chaotic time series, but also replicates their long-term ergodic properties. Optical NGRC shows superiority in shorter training length, increased interpretability and fewer hyperparameters compared to conventional optical RC based on scattering media, while achieving better forecasting performance. Our optical NGRC framework may inspire the realization of NGRC in other physical RC systems, new applications beyond time-series processing, and the development of deep and parallel architectures broadly.

physics.optics↗

arXiv2024

Fourier Synthetic Aperture-based Time-resolved Terahertz Imaging

Terahertz microscopy has attracted attention owing to distinctive characteristics of the THz frequency region, particularly non-ionizing photon energy, spectral fingerprint, and transparency to most nonpolar materials. Nevertheless, the well-known Rayleigh diffraction limit imposed on THz waves commonly constrains the resultant imaging resolution to values beyond the millimeter scale, consequently limiting the applicability in numerous emerging applications for chemical sensing and complex media imaging. In this theoretical and numerical work, we address this challenge by introducing a new imaging approach, based on acquiring high-spatial frequencies by adapting the Fourier synthetic aperture approach to the terahertz spectral range, thus surpassing the diffraction-limited resolution. Our methodology combines multi-angle terahertz pulsed illumination with time-resolved field measurements, as enabled by the state-of-the-art time-domain spectroscopy technique. We demonstrate the potential of the approach for hyperspectral terahertz imaging of semi-transparent samples and show that the technique can reconstruct spatial and temporal features of complex inhomogeneous samples with subwavelength resolution.

physics.optics↗

arXiv2024

Correlating light fields through disordered media across multiple degrees of freedom

Speckle patterns are inherent features of coherent light propagation through complex media. As a result of interference, they are sensitive to multiple experimental parameters such as the configuration of disorder or the propagating wavelength. Recent developments in wavefront shaping have made it possible to control speckle pattern statistics and correlations, for example using the concept of the transmission matrix. In this article, we address the problem of correlating scattered fields across multiple degrees of freedom. We highlight the common points between the specific techniques already demonstrated, and we propose a general framework based on the singular value decomposition of a linear combination of multiple transmission matrices. Following analytical predictions, we experimentally illustrate the technique on spectral and temporal correlations, and we show that both the amplitude and the phase of the field correlations can be tuned. Our work opens up new perspectives in speckle correlation manipulation, with potential applications in coherent control.

physics.optics↗

arXiv2024

Demixing fluorescence time traces transmitted by multimode fibers

Optical methods based on thin multimode fibers (MMFs) are promising tools for measuring neuronal activity in deep brain regions of freely moving mice thanks to their small diameter. However, current methods are limited: while fiber photometry provides only ensemble activity, imaging techniques using of long multimode fibers are very sensitive to bending and have not been applied to unrestrained rodents yet. Here, we demonstrate the fundamentals of a new approach using a short MMF coupled to a miniscope. In proof-of-principle in vitro experiments, we disentangled spatio-temporal fluorescence signals from multiple fluorescent sources transmitted by a thin (200 μm) and short (8 mm) MMF, using a general unconstrained non-negative matrix factorization algorithm directly on the raw video data. Furthermore, we show that low-cost open-source miniscopes have sufficient sensitivity to image the same fluorescence patterns seen in our proof-of-principle experiment, suggesting a new avenue for novel minimally invasive deep brain studies using multimode fibers in freely behaving mice.

physics.optics↗

arXiv2024

Training of Physical Neural Networks

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models.

physics.app-ph↗

arXiv2024

Replica-assisted super-resolution fluorescence imaging in scattering media

Far-field super-resolution fluorescence microscopy has been rapidly developed for applications ranging from cell biology to nanomaterials. However, it remains a significant challenge to achieve super-resolution imaging at depth in opaque materials. In this study, we present a super-resolution microscopy technique for imaging hidden fluorescent objects through scattering media, started by exploiting the inherent object replica generation arising from the memory effect, i.e. the seemingly informationless emission speckle can be regarded as a random superposition of multiple object copies. Inspired by the concept of super-resolution optical fluctuation imaging, we use temporally-fluctuating speckles to excite fluorescent signals and perform high-order cumulant analysis on the fluctuation, which can not only improve the image resolution, but also increase the speckle contrast to isolate only the bright object replicas. A super-resolved image can be finally retrieved by simply unmixing the sparsely distributed replicas with their location map. This methodology allows to overcome scattering and achieve robust super-resolution fluorescence imaging, circumventing the need of heavy computational steps.

physics.optics↗

arXiv2024

Neurophotonics beyond the Surface: Unmasking the Brain's Complexity Exploiting Optical Scattering

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

physics.optics↗

arXiv2024

Generalized angle-orbital-angular-momentum Talbot effect and modulo mode sorting

The Talbot effect describes periodic revivals of field patterns and is ubiquitous across wave systems. In optics, it is mostly known for its manifestations in space and time, but is also observed in the wavevector and frequency spectra owing to the Fourier duality. Recently, the Talbot self-imaging has been shown separately in the azimuthal angle and orbital angular momentum (OAM) domains. Here, we unveil the missing link between them and demonstrate the generalized angle-OAM Talbot effect. Versatile transformations of petal fields and OAM spectra are experimentally showcased, based on the synergy of angular Talbot phase modulation and light propagation in a ring-core fiber. Moreover, the generalized self-imaging concept leads to new realizations in mode sorting, which separate OAM modes in a modulo manner, theoretically free from any crosstalk within the congruence classes of OAM modes. We design and experimentally construct various mode sorters with excellent performance, and show the unconventional behavior of Talbot-based sorters where neighboring OAM modes can be mapped to positions far apart. Besides its fundamental interest, our work finds applications in OAM-based information processing, and implies that the physical phenomena in time-frequency and angle-OAM domains are broadly connected as well as their processing techniques may be borrowed interchangeably.

physics.optics↗

arXiv2023

Classification of quantum states of light using random measurements through a multimode fiber

Extracting meaningful information about unknown quantum states without performing a full tomography is an important task. Low-dimensional projections and random measurements can provide such insight but typically require careful crafting. In this paper, we present an optical scheme based on sending unknown input states through a multimode fiber and performing two-point intensity and coincidence measurements. A short multimode fiber implements effectively a random projection in the spatial domain, while a long-dispersive multimode fiber performs a spatial and spectral projection. We experimentally show that useful properties -- i.e., the purity, dimensionality, and degree of indistinguishability -- of various states of light including spectrally entangled biphoton states, can be obtained by measuring statistical properties of photocurrents and their correlation between two outputs over many realizations of unknown random projections. Moreover, we show that this information can then be used for state classification.

quant-ph↗

arXiv2023

Large-scale photonic computing with nonlinear disordered media

Neural networks find widespread use in scientific and technological applications, yet their implementations in conventional computers have encountered bottlenecks due to ever-expanding computational needs. Photonic neuromorphic hardware, which manipulates information and represents data continuously in the optical domain, is one of the promising platforms with potential advantages of massive parallelism, ultralow latency, and reduced energy consumption. While linear photonic neural networks are within reach, photonic computing with large-scale optical nonlinear nodes remains largely unexplored. Here, we demonstrate a large-scale, high-performance nonlinear photonic neural system based on a disordered polycrystalline slab composed of lithium niobate nanocrystals. Mediated by random quasi-phase-matching and multiple scattering, linear and nonlinear optical speckle features are generated as the interplay between the simultaneous linear random scattering and the second-harmonic generation, defining a complex neural network in which the second-order nonlinearity acts as internal nonlinear activation functions. Benchmarked against linear random projection, such nonlinear mapping embedded with rich physical computational operations shows improved performance across a large collection of machine learning tasks in image classification, regression, and graph classification with varying complexity. Demonstrating up to 27,648 input and 3,500 nonlinear output nodes, the combination of optical nonlinearity and random scattering serves as a scalable computing engine for diverse applications.

physics.optics↗

arXiv2023

Patchy landscapes promote stability of small groups

Group formation and coordination are fundamental characteristics of living systems, essential for performing tasks and ensuring survival. Interactions between individuals play a key role in group formation, and the impact of resource distributions is a vibrant area of research. Using active particles in a tuneable optical environment as a model system, we demonstrate that heterogeneous energy source distributions result in smaller, more stable groups with reduced individual exchange between clusters compared to homogeneous conditions. Reduced group sizes can be beneficial to optimise resources in heterogeneous environments and to control information flow within populations. Devoid of biological complications, our system provides insights into the importance of patchy landscapes in ecological dynamics and holds implications for refining swarm intelligence algorithms and enhancing crowd control techniques.

cond-mat.soft↗

arXiv2023

An optical Ising spin glass simulator with tuneable short range couplings

Non-deterministic polynomial-time (NP) problems are ubiquitous in almost every field of study. Recently, all-optical approaches have been explored for solving classic NP problems based on the spin-glass Ising Hamiltonian. However, obtaining programmable spin-couplings in large-scale optical Ising simulators, on the other hand, remains challenging. Here, we demonstrate control of the interaction length between user-defined parts of a fully-connected Ising system. This is achieved by exploiting the knowledge of the transmission matrix of a random medium and by using diffusers of various thickness. Finally, we exploit our spin-coupling control to observe replica-to-replica fluctuations and its analogy to standard replica symmetry breaking.

cond-mat.dis-nn↗

arXiv2023

Phase conjugation with spatially incoherent light in complex media

Shaping light deep inside complex media, such as biological tissue, is critical to many research fields. Although the coherent control of scattered light via wavefront shaping has made significant advances in addressing this challenge, controlling light over extended or multiple targets without physical access inside a medium remains elusive. Here we present a phase conjugation method for spatially incoherent light, which enables the non-invasive light control based on incoherent emission from multiple target positions. Our method characterizes the scattering responses of hidden sources by retrieving mutually incoherent scattered fields from speckle patterns. By time-reversing scattered fluorescence with digital phase conjugation, we experimentally demonstrate focusing of light on individual and multiple targets. We also demonstrate maximum energy delivery to an extended target through a scattering medium by exploiting transmission eigenchannels. This paves the way to control light propagation in complex media using incoherent contrasts mechanisms.

physics.optics↗

arXiv2023

Large Reconfigurable Quantum Circuits with SPAD Arrays and Multimode Fibers

Reprogrammable linear optical circuits are essential elements of photonic quantum technology implementations. Integrated optics provides a natural platform for tunable photonic circuits, but faces challenges when high dimensions and high connectivity are involved. Here, we implement high-dimensional linear transformations on spatial modes of photons using wavefront shaping together with mode mixing in a multimode fiber, and measure photon correlations using a time-tagging single-photon avalanche diode (SPAD) array. In order to prove the suitability of our approach for quantum technologies we demonstrate two-photon interferences in a tunable complex linear network -- a generalization of a Hong-Ou-Mandel interference to 22 output ports. We study the scalability of our approach by quantifying the similarity between the ideal photon correlations and the correlations obtained experimentally for various linear transformations. Our results demonstrate the potential of wavefront shaping in complex media in conjunction with SPAD arrays for implementing high-dimensional reconfigurable quantum circuits. Specifically, we achieved $(80.5 \pm 6.8)\%$ similarity for indistinguishable photon pairs and $(84.9 \pm 7.0)\%$ similarity for distinguishable photon pairs using 22 detectors and random circuits. These results emphasize the scalability and reprogrammable nature of our approach.

quant-ph↗

arXiv2023

Linear Optical Random Projections Without Holography

We introduce a novel method to perform linear optical random projections without the need for holography. Our method consists of a computationally trivial combination of multiple intensity measurements to mitigate the information loss usually associated with the absolute-square non-linearity imposed by optical intensity measurements. Both experimental and numerical findings demonstrate that the resulting matrix consists of real-valued, independent, and identically distributed (i.i.d.) Gaussian random entries. Our optical setup is simple and robust, as it does not require interference between two beams. We demonstrate the practical applicability of our method by performing dimensionality reduction on high-dimensional data, a common task in randomized numerical linear algebra with relevant applications in machine learning.

physics.optics↗