Light, science & applications最新文献

In non-Hermitian systems, the dynamic encircling of exceptional points (EPs) engenders intriguing chiral phenomena, where the resultant state characteristics are intrinsically dependent upon the encircling handedness. An ingenious approach using simple leaky optical elements has been presented to emulate this chiral behavior without physically encircling an EP. This innovative simplification of EP properties enables a more straightforward implementation of asymmetric switching of polarization and path. Given that photons inherently possess multiple physical degrees of freedom, the research focus has shifted from single-dimensional to multidimensional asymmetric switching. Hence, there is a fundamental challenge of how to achieve multidimensional asymmetric switching through a simple and universally applicable architecture. Here, we propose and experimentally demonstrate a novel topology-optimized architecture, termed EP-encirclement emulation tailoring, enabling multidimensional asymmetric switching. Theoretical analysis reveals that our architecture eliminates the 3-dB inherent loss in conventional architecture by replacing couplers with (de)multiplexers. Building upon this architecture, we harness all-fiber devices to implement a high-performance asymmetric switching of polarization, mode, and orbital angular momentum (OAM). To our knowledge, this is the first experimental demonstration of asymmetric OAM switching to date. Our work provides an efficient topology architecture for emulating dynamic EP encirclement, paving the way for universal and flexible asymmetric switching devices.

Distributed fiber-optic sensing has become an indispensable tool for large-scale structural and environmental monitoring, where spectral interrogation of backscattering light enables high-precision quantitative measurement of external perturbations. Conventional spectral analysis methods, typically based on frequency-domain serial interrogation or time-to-frequency mapping, face inherent trade-offs between measurement speed, dynamic strain measurement range, and system complexity. Here, we present a distributed frequency comb enabled spectrum-correlation reflectometry as a universal spectral analysis framework that leverages optical frequency comb for parallel multi-frequency interrogation, which is experimentally demonstrated in a phase-sensitive optical time-domain reflectometry (φ-OTDR) system. This method eliminates the need for large frequency scans, achieving more than tenfold improvement in measurement speed over the state-of-the-art spectral analysis methods. Compared to existing phase-demodulated φ-OTDR systems, this method enables vibration amplitude monitoring with a dynamic strain measurement range expanded by more than an order of magnitude, while intrinsically circumventing phase unwrapping issues and interference fading. This work establishes a new paradigm for distributed spectral analysis, providing a flexible and robust platform for a wide range of sensing technologies, including Rayleigh and Brillouin-based schemes, which may have significant impact for geophysics, seismology, civil engineering, and other fields.

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.

Accurate wavelength measurement is critical for spectroscopy, optical communications, semiconductor manufacturing, and quantum research. Emerging reconstructive wavemeters are compact, cost-effective devices that utilize pseudo-random wavelength patterns and computational techniques to provide high-resolution, broadband alternatives to solutions based on frequency beating and interferometry. We propose a novel reconstructive wavemeter that synergizes the advantages of both approaches. Its physical model is based on the integration of thousands of high-quality-factor optical microcavities, which are deformed to stimulate whispering gallery mode splitting. For realizing a wavelength interpreter, we developed a hybrid machine learning approach utilizing boosting methods and variational autoencoders. This enabled the implementation of wavelength interpretation as a rigorous regression task for the first time. The introduced novel concept ensures the uniqueness of the wavelength patterns up to ultra-wide (~100 nm) spectral window while guarantees high (~100 fm) intrinsic sensitivity. The latter allocates the proposed solution right next to the ultimate reconstructive wavemeters based on integrating spheres, but with less calibration efforts, featuring superior miniaturization options and chip-scale integrability.

Ising machines offer a paradigm shift from traditional computing methods, tackling complex combinatorial optimization problems (COPs). Despite the proliferation of various Ising machine implementations, their application to solve real-world COPs has been limited. Here, we introduce a high-performance optoelectronic Ising machine (OEIM), based on optoelectronic parametric oscillators, that represents a significant advancement in this field. With 4096 Ising spins, arbitrary coupling capabilities, and unparalleled long-term stability, our OEIM outperforms traditional computing approaches in both accuracy and speed. By solving the benchmark maximum cut problem, we demonstrate its superior performance. More importantly, we apply the OEIM to a real-world traffic optimization problem, using real traffic data and a classical traffic model, and achieve results that far surpass those of conventional computers. This work not only validates the OEIM's capability to solve complex practical challenges but also heralds a new era in real-time traffic management, where high-performance optoelectronic Ising machines enable rapid and efficient solutions to critical societal issues.

Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems. Conventional photodetectors often suffer from a weak photoresponse and a linear dependence of photocurrent on light intensity, which restricts their ability to capture low-contrast features and makes them susceptible to noise. Inspired by the adaptive mechanisms of the human visual system, we present a molybdenum disulfide (MoS₂) phototransistor with tunable sensitivity, in which the gate stack incorporates a heterostructure diode-composed of O-plasma-treated MoS₂ and pristine MoS₂-that serves as the photosensitive layer. This configuration enables light-intensity-dependent modulation of the diode's conductance, which dynamically in turn alters the voltage distribution across the gate dielectric and transistor channel, leading to a significant photoresponse. By modulating the gate voltage, the light response range can be finely tuned, maintaining high sensitivity to low-contrast targets while suppressing noise interference. Compared to conventional photodetectors, the proposed device achieves a 1000-fold improvement in sensitivity for low-contrast signal detection and exhibits significantly enhanced noise immunity. The intelligent machine vision system built on this device demonstrates exceptional performance in detecting low-contrast targets, underscoring its promise for next-generation machine vision applications.

In this paper, we provide an overview and comparison of devices used for optical waveguide-to-waveguide coupling including inter-chip edge couplers, grating couplers, free form couplers, evanescent couplers, cantilever couplers, and optical wirebonds. In addition, technology for efficient transmission of light through chips is discussed including guided mode and free form photonic vias for substrates including silicon, glass, and organics. The results are discussed in the context of potential applications including co-packaged optics switch packages, replaceable biochemical sensors, optically connected memory, optical computing, integrated quantum photonics, and integrated LiDAR systems to show possible improvements in energy efficiency, performance, and cost.

Quantum walks with one-dimensional translational symmetry are important for quantum algorithms, where the speed-up of the diffusion speed can be reached if long-range couplings are added. Our work studies a scheme of a ring under the strong resonant modulation that can support a discrete-time quantum walk including coherent multiple long-range translations in a natural way along the synthetic frequency dimension. These multiple translation paths are added in a coherent way, which makes the walker evolve under the topological band. Therein, not only the fast diffusion speed is expected, but more importantly, we find that single quantum gate operations can be performed in the quasi-momentum space. In particular, we show the arbitrary single-qubit state preparation and an example of CNOT two-qubit gate with only one time step, dramatically increasing quantum algorithms. Our study uses the modulated ring to provide fast quantum gate operations based on coherent multiple path quantum walk, which may provide unique designs for efficient quantum operations on photonic chips.

Deployment of terahertz communication and spectroscopy systems relies on the availability of low-noise and fast detectors, with plug-and-play capabilities. However, most current technologies are stand-alone, discrete components. They are often slow or susceptible to temperature drifts and require tight beam focusing to maximize the signal-to-noise of the detector. Here, we demonstrate an integrated photonic architecture in thin-film lithium niobate that addresses these challenges by exploiting the electro-optic modulation induced by a terahertz signal onto an optical beam at telecom frequencies. Leveraging on the low optical losses provided by this platform, we integrate a double array of up to 18 terahertz antennas within a Mach-Zehnder interferometer, considerably extending the device collection area and boosting the interaction efficiency between the terahertz signal and the optical beam. We show that the double array coherently builds up the probe modulation through a mechanism of quasi-phase-matching, driven by a periodic terahertz near-field pattern, circumventing physical inversion of the crystallographic domains. This provides means to fully custom-tailor the frequency response of the device, limit it to a desired frequency band and effectively suppress out-of-band signals. The large detection area ensures correct operation with diverse terahertz beam settings. Furthermore, we show that the antennas act as pixels that allow reconstruction of the terahertz beam profile impinging on the detector area. Our on-chip design in thin-film lithium niobate overcomes the detrimental effects of two-photon absorption and fixed phase-matching conditions, which have plagued previously explored electro-optic detection systems, especially in the telecom band, paving the way for more advanced on-chip terahertz systems.

Photonics is promising to handle extensive vector multiplications in artificial intelligence (AI) techniques due to natural bosonic parallelism and high-speed information transmission. However, the dimensionality of current photonic linear operation is limited and tough to improve due to the complex beam interaction for implementing optical matrix operation and digital-analog conversions. Here, we propose a programmable and reconfigurable photonic linear vector machine with extreme scalability formed by a series of emitter-detector pairs as the independent basic computing units. The elemental values of two high-dimensional vectors are prepared on emitter-detector pairs by bit encoding and analog detecting method without requiring large-scale analog-to-digital converter or digital-to-analog converter arrays. Since there is no interaction among light beams inside, extreme scalability could be achieved by simply multiplicating the independent emitter-detector pair. The proposed architecture is inspired by the traditional Chinese Suanpan or abacus, and thus is denoted as photonic SUANPAN. Experimentally, the computing fidelities for vector inner products could achieve >98% in our implementation with an 8 × 8 vertical cavity surface emission laser (VCSEL) array and an 8 × 8 MoTe₂ two-dimensional material photodetector array. Furthermore, such implementation is applied on two typical AI tasks as 1024-dimensional optimization problem is successfully solved and competitive classification accuracy of 88% is achieved for handwritten digit dataset. We believe that the photonic SUANPAN could serve as a fundamental linear vector machine and enhance various future AI applications.