RESUMEN
Bessel beam arrays are highly attractive due to non-diffraction properties, parallel processing, and large capacity capabilities. However, conventional approaches of generating Bessel beams, such as spatial light modulators, axicons, and diffraction optical elements, suffer from various limitations of system complexity and bulkiness, low uniformity, and limited numerical aperture (NA). The limited NA imposes constraints on achieving minimal full width at half maximum (FWHM) of the Bessel beam, ultimately compromising the resolution of the beam. In this study, we demonstrate a method for generating Bessel beam arrays with regular and random patterns via an ultra-compact metasurface. This approach integrates the phase profile of an optimized beam splitter with a meta-axicon. The Bessel beam arrays exhibit subwavelength dimensions of FWHM (590 nm, â¼0.9λ) and relatively high uniformity of 90% for N A=0.2 and 69% for N A=0.4. Furthermore, the method achieves effective suppression of background noise and zeroth-order intensity compared to methods based on Dammann grating (DG) based metasurfaces. The proposed method highlights potential applications of Bessel beam arrays in various fields, such as laser machining, optical communication, and biomedical imaging.
RESUMEN
Highly reflective surfaces are notorious in the field of depth sensing and three-dimensional (3D) imaging because they can cause severe errors in perception of the depth. Despite recent progress in addressing this challenge, there are still no robust and error-free solutions. Here, we devise a polarization structured light 3D sensor for solving these problems, in which high-contrast-grating (HCG) vertical-cavity surface-emitting lasers (VCSELs) are used to exploit the polarization property. We demonstrate accurate depth measurements of the reflective surfaces and objects behind them in various imaging situations. In addition, the absolute error and effective measurement range are measured to prove the applicability for a wide range of 3D applications. Our work innovatively combines polarization and depth information, opening the way for fully understanding and applying polarization properties in the 3D domain.
RESUMEN
The implementation of quantum information technologies requires the development of integrated quantum chips. Femtosecond laser direct writing (FLDW) waveguides and superconducting nanowire single-photon detectors (SNSPDs) have been widely applied in integrated quantum photonic circuits. In this work, a novel FLDW waveguide-coupled SNSPD was designed and realized by integrating FLDW waveguides and conventional SNSPDs together. Through a COMSOL simulation, a waveguide end face-nanowire optical coupling structure was designed and verified. The simulation results showed that the FLDW waveguide-coupled SNSPD device, which had a target wavelength of 780â nm, can achieve 87% optical absorption. Then the preparation process of the FLDW waveguide-coupled SNSPD device was developed, and the fabricated device achieved a system detection efficiency of 1.7% at 10â Hz dark count rate. Overall, this method provides a feasible single-photon detector solution for future on-chip integrated quantum photonic experiments and applications.
RESUMEN
The subset sum problem (SSP) is a typical nondeterministic-polynomial-time (NP)-complete problem that is hard to solve efficiently in time with conventional computers. Photons have the unique features of high propagation speed, strong robustness, and low detectable energy level and therefore can be promising candidates to meet the challenge. Here, we present a scalable chip built-in photonic computer to efficiently solve the SSP. We map the problem into a three-dimensional waveguide network through a femtosecond laser direct writing technique. We show that the photons sufficiently dissipate into the networks and search for solutions in parallel. In the case of successive primes, our approach exhibits a dominant superiority in time consumption even compared with supercomputers. Our results confirm the ability of light to realize computations intractable for conventional computers, and suggest the SSP as a good benchmarking platform for the race between photonic and conventional computers on the way toward "photonic supremacy."
RESUMEN
Quantum process tomography is often used to completely characterize an unknown quantum process. However, it may lead to an unphysical process matrix, which will cause the loss of information with respect to the tomography result. Convex optimization, widely used in machine learning, is able to generate a global optimum that best fits the raw data while keeping the process tomography in a legitimate region. Only by correctly revealing the original action of the process can we seek deeper into its properties like its phase transition and its Hamiltonian. Here, we reconstruct the seawater channel using convex optimization and further test it on the seven fundamental gates. We compare our method to the standard-inversion and norm-optimization approaches using the cost function value and our proposed state deviation. The advantages convince that our method enables a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources. In addition, we examine on a set of non-unitary channels and the reconstructions reach up to 99.5% accuracy. Our method offers a more universal tool for further analyses on the components of the quantum channels and we believe that the crossover between quantum process tomography and convex optimization may help us move forward to machine learning of quantum channels.