Esteban Gómez-López, Dominik Ritter, Jisoo Kim
et al.
Quantum memories are essential for photonic quantum technologies, enabling long-distance quantum communication and serving as delay units in quantum computing. Hot atomic vapors using electromagnetically induced transparency provide a simple platform with second-long photon storage capabilities. Light-guiding structures enhance performance, but current hollow-core fiber waveguides face significant limitations in filling time, physical size, fabrication versatility, and large-scale integration potential. In this work, we demonstrate the storage of attenuated coherent light pulses in a cesium (Cs) quantum memory based on a 3D-nanoprinted hollow-core waveguide, known as a light cage (LC), with several hundred nanoseconds of storage times. Leveraging the versatile fabrication process, we successfully integrated multiple LC memories onto a single chip within a Cs vapor cell, achieving consistent performance across all devices. We conducted a detailed investigation into storage efficiency, analyzing memory lifetime and bandwidth. These results represent a significant advancement toward spatially multiplexed quantum memories and have the potential to elevate memory integration to unprecedented levels. We anticipate applications in parallel single-photon synchronization for quantum repeater nodes and photonic quantum computing platforms.
Solvay Blomquist, Hubert Martin, Hyukmo Kang
et al.
In the development of space-based large telescope systems, having the capability to perform active optics correction allows correcting wavefront aberrations caused by thermal perturbations so as to achieve diffraction-limited performance with relaxed stability requirements. We present a method of active optics correction used for current ground-based telescopes and simulate its effectiveness for a large honeycomb primary mirror in space. We use a finite-element model of the telescope to predict misalignments of the optics and primary mirror surface errors due to thermal gradients. These predicted surface error data are plugged into a Zemax ray trace analysis to produce wavefront error maps at the image plane. For our analysis, we assume that tilt, focus and coma in the wavefront error are corrected by adjusting the pointing of the telescope and moving the secondary mirror. Remaining mid- to high-order errors are corrected through physically bending the primary mirror with actuators. The influences of individual actuators are combined to form bending modes that increase in stiffness from low-order to high-order correction. The number of modes used is a variable that determines the accuracy of correction and magnitude of forces. We explore the degree of correction that can be made within limits on actuator force capacity and stress in the mirror. While remaining within these physical limits, we are able to demonstrate sub-25 nm RMS surface error over 30 hours of simulated data. The results from this simulation will be part of an end-to-end simulation of telescope optical performance that includes dynamic perturbations, wavefront sensing, and active control of alignment and mirror shape with realistic actuator performance.
Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.
Although the temperature of a thermodynamic system is usually believed to be a positive quantity, under particular conditions, negative temperature equilibrium states are also possible. Negative temperature equilibriums have been observed with spin systems, cold atoms in optical lattices and two-dimensional quantum superfluids. Here we report the observation of Rayleigh-Jeans thermalization of light waves to negative temperature equilibrium states. The optical wave relaxes to the equilibrium state through its propagation in a multimode optical fiber, i.e., in a conservative Hamiltonian system. The bounded energy spectrum of the optical fiber enables negative temperature equilibriums with high energy levels (high order fiber modes) more populated than low energy levels (low order modes). Our experiments show that negative temperature speckle beams are featured, in average, by a non-monotonous radial intensity profile. The experimental results are in quantitative agreement with the Rayleigh-Jeans theory without free parameters. Bringing negative temperatures to the field of optics opens the door to the investigation of fundamental issues of negative temperature states in a flexible experimental environment.
Nonreciprocal optical devices have broad applications in light manipulations for communications and sensing. Non-magnetic mechanisms of optical nonreciprocity are highly desired for high-frequency on-chip applications. Here, we investigate the nonreciprocal properties of light propagation in a dielectric waveguide induced by a subwavelength spinning cylinder. We find that the chiral modes of the cylinder can give rise to unidirectional coupling with the waveguide via the transverse spin-orbit interaction, leading to different transmissions for guided wave propagating in opposite directions and thus optical isolation. We reveal the dependence of the nonreciprocal properties on various system parameters including mode order, spinning speed, and coupling distance. The results show that higher-order chiral modes and larger spinning speed generally give rise to stronger nonreciprocity, and there exists an optimal cylinder-waveguide coupling distance where the optical isolation reaches the maximum. Our work contributes to the understanding of nonreciprocity in subwavelength moving structures and can find applications in integrated photonic circuits, topological photonics, and novel metasurfaces.
G. K. Alagashev, S. S. Stafeev, V. V. Kotlyar
et al.
The optical properties of solid core microstructured optical fibers (SC MOFs) have been studied for a long time. The process of energy outflow of the core modes has always been associated with the process of constructive interference of the core modes fields under reflection from the photonic crystal cladding. In this paper, we want to offer a new look at the light localization in the core of SC MOFs related to the behavior of spin and orbital parts of the Poynting vector of these core modes and singularities arising in it.
E. P. McShane, H. K. Chandrasekharan, A. Kufcsák
et al.
We report a time-resolved single photon counting (TCSPC) imaging system based on a line-scanning architecture. The system benefits from the high fill-factor, active area, and large dimension of an advanced CMOS single photon avalanche diode (SPAD) array line-sensor. A two-dimensional image is constructed using a moving mirror to scan the line-sensor field-of-view (FOV) across the target, to enable the efficient acquisition of a two-dimensional 0.26 Mpixel TCSPC image. We demonstrate the capabilities of the system for TCSPC imaging and locating objects obscured in scattering media - specifically to locate a series of discrete point sources of light along an optical fibre submerged in a highly scattering solution. We demonstrate that by selectively imaging using early arriving photons which have undergone less scattering than later arriving photons, our TCSPC imaging system is able to locate the position of discrete point sources of light than a non-time-resolved imaging system.
Scattering immune propagation of light in topological photonic systems may revolutionarize the design of integrated photonic circuits for information processing and communications. In optics, various photonic topological circuits have been developed, which were based on classical emulation of either quantum spin Hall effect or quantum valley Hall effect. On the other hand, the combination of both the valley and spin degrees of freedom can lead to a new kind of topological transport phenomenon, dubbed quantum spin valley Hall effect (QSVH), which can further expand the number of topologically protected edge channels and would be useful for information multiplexing. However, it is challenging to realize QSVH in most known material platforms, due to the requirement of breaking both the (pseudo-)fermionic time-reversal (T) and parity symmetries (P) individually, but leaving the combined symmetry S=TP intact. Here, we propose an experimentally feasible platform to realize QSVH for light, based on coupled ring resonators mediated by optical Kerr nonlinearity. Thanks to the inherent flexibility of cross-mode modulation (XMM), the coupling between the probe light can be engineered in a controllable way such that spin-dependent staggered sublattice potential emerges in the effective Hamiltonian. With delicate yet experimentally feasible pump conditions, we show the existence of spin valley Hall induced topological edge states. We further demonstrate that both degrees of freedom, i.e., spin and valley, can be manipulated simultaneously in a reconfigurable manner to realize spin-valley photonics, doubling the degrees of freedom for enhancing the information capacity in optical communication systems.
Mohammed Suhail, Carlos Esteves, Leonid Sigal
et al.
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects. We introduce a model that combines the strengths and mitigates the limitations of these two directions. By operating on a four-dimensional representation of the light field, our model learns to represent view-dependent effects accurately. By enforcing geometric constraints during training and inference, the scene geometry is implicitly learned from a sparse set of views. Concretely, we introduce a two-stage transformer-based model that first aggregates features along epipolar lines, then aggregates features along reference views to produce the color of a target ray. Our model outperforms the state-of-the-art on multiple forward-facing and 360° datasets, with larger margins on scenes with severe view-dependent variations.
Vladimir Semenov, Xavier Porte, Ibrahim Abdulhalim
et al.
Nonlinear spatio-temporal systems are the basis for countless physical phenomena in such diverse fields as ecology, optics, electronics and neuroscience. The canonical approach to unify models originating from different fields is the normal form description, which determines the generic dynamical aspects and different bifurcation scenarios. Realizing different types of dynamical systems via one experimental platform that enables continuous transition between normal forms through tuning accessible system parameters is therefore highly relevant. Here, we show that a transmissive, optically-addressed spatial light modulator under coherent optical illumination and optical feedback coupling allows tuning between pitchfork, transcritical and saddle-node bifurcations of steady states. We demonstrate this by analytically deriving the system's normal form equations and confirm these results via extensive numerical simulations. Our model describes a nematic liquid crystal device using nano-dimensional dichalcogenide (a-As$_2$S$_3$) glassy thin-films as photo sensors and alignment layers, and we use device parameters obtained from experimental characterization. Optical coupling, for example using diffraction, holography or integrated unitary maps allow implementing a variety of system topologies of technological relevance for neural networks and potentially XY-Hamiltonian models with ultra low energy consumption.
Light-front holography offers a successful first semiclassical approximation to hadronic spectroscopy and dynamics. We review its underlying assumptions, its remarkable predictions as well as attempts to go beyond the semiclassical approximation in order to the describe a wide range of data with a universal AdS/QCD mass scale.
M. Arshadi Pirlar, M. Rezaei Mirghaed, Y. Honarmand
et al.
In this paper, we examine the light scattering by the flow of levitated flakes in a micro-channel to characterize the tunable functionality of the graphene oxide liquid crystal in the nematic phase. Light interaction with the mentioned material is decomposed to the scattered and transmitted parts and they can determine the orientation of the flakes. Our results demonstrate that, pumping the graphene oxide sample through the micro-channel leads to increase the amplitude of scattered light. The time averaged of scattered light intensity grows by increasing volume fraction. We also find that, the higher volume fraction, the sooner reaching to saturated normalized scattered intensity is. To get deep insight about our experimental results, we rely on the general theoretical properties of the light scattering cross-section incorporating the fluctuation of director vector and dielectric tensor. Our proposal is a promising approach to carry out the mechanical-hydrodynamical approach for controlling the orientation of a typical liquid crystal.
Coherent optical multi-carrier communications have recently dominated metro-regional and long-haul optical communications. However, the major obstacle of networks involving coherent multi-carrier signals such as coherent optical orthogonal frequency-division multiplexing (CO-OFDM) is the fiber-induced nonlinearity and the parametric noise amplification from cascaded optical amplifiers which results in significant nonlinear distortion among subcarriers. Here, we present the first nonlinear equalizer in optical communications using the traditional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a novel modified version of DBSCAN which combines K-means clustering on the noisy un-clustered symbols. For a 24.72 Gbit/sec differential quaternary phase-shift keying (DQPSK) CO-OFDM system, the modified DBSCAN can increase the signal quality-factor by up to 2.158 dB compared to linear equalization at 500 km of transmission. The modified DBSCAN slightly outperforms the traditional DBSCAN, fuzzy-logic C-means, hierarchical and conventional K-means clustering at high launched optical powers.
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tunable, single passband as well as dual passband spectral filters, and (2) spatially-controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep learning-based design strategy, broadband diffractive neural networks help us engineer light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.
To the best of our knowledge, for the first time, we propose adaptive moment estimation (Adam) algorithm based on batch gradient descent (BGD) to design a time-domain equalizer (TDE) for PAM-based optical interconnects. Adam algorithm has been widely applied in the fields of artificial intelligence. For TDE, BGD-based Adam algorithm can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Therefore, fast and stable convergence can be achieved by BGD-based Adam algorithm with low mean square error. Meanwhile, BGD-based Adam algorithm is implemented by parallel processing, which is more efficient than conventional serial algorithms, such as least mean square and recursive least square algorithms. The experimental results demonstrate that BGD-based Adam feed-forward equalizer works well in 120-Gbit/s PAM8 optical interconnects. In conclusion, BGD-based Adam algorithm shows great potential for converging the tap coefficients of TDE in future optical interconnects.
Quantum trapping potentials for ultracold gases change the landscape of classical properties of scattered light and matter. The atoms in a quantum many-body correlated phase of matter change the properties of light and vice versa. The properties of both light and matter can be tuned by design and depend on the interplay between long-range (nonlocal) interactions mediated by an optical cavity and short-range processes of the atoms. Moreover, the quantum properties of light get significantly altered by this interplay, leading the light to have nonclassical features. Further, these nonclassical features can be designed and optimised.