Because of their ultra-light, ultra-thin, and flexible design, metalenses exhibit significant potential in the development of highly integrated cameras. However, the performances of metalens-integrated camera are constrained by their fixed architectures. Here we proposed a high-quality imaging method based on deep learning to overcome this constraint. We employed a multi-scale convolutional neural network (MSCNN) to train an extensive pair of high-quality and low-quality images obtained from a convolutional imaging model. Through our method, the imaging resolution, contrast, and distortion have all been improved, resulting in a noticeable overall image quality with SSIM over 0.9 and an improvement in PSNR over 3â dB. Our approach enables cameras to combine the advantages of high integration with enhanced imaging performances, revealing tremendous potential for a future groundbreaking imaging technology.
The quantum denoising technology efficiently removes noise from images; however, the existing algorithms are only effective for additive noise and cannot remove multiplicative noise, such as speckle noise in synthetic aperture radar (SAR) images. In this paper, based on the grayscale morphology method, a quantum SAR image denoising algorithm is proposed, which performs morphological operations on all pixels simultaneously to remove the noise in the SAR image. In addition, we design a feasible quantum adder to perform cyclic shift operations. Then, quantum circuits for dilation and erosion are designed, and the complete quantum circuit is then constructed. For a 2n×2n quantum SAR image with q grayscale levels, the complexity of our algorithm is O (n+q). Compared with classical algorithms, it achieves exponential improvement and also has polynomial-level improvements than existing quantum algorithms. Finally, the feasibility of our algorithm is validated on IBM Q.
Photon-electron interactions are essential for many areas such as energy conversion, signal processing, and emerging quantum science. However, the current demonstrations are typically targeted to fiber and on-chip applications and lack of study in wave space. Here, we introduce a concept of optoelectronic metasurface that is capable of realizing direct and efficient optical-microwave interactions in free space. The optoelectronic metasurface is realized via a hybrid integration of microwave resonant meta-structures with a photoresponsive material. As a proof of concept, we construct an ultrathin optoelectronic metasurface using photodiodes that is bias free, which is modeled and analyzed theoretically by using the light-driven electronic excitation principle and microwave network theory. The incident laser and microwave from the free space will interact with the photodiode-based metasurface simultaneously and generate strong laser-microwave coupling, where the phase of output microwave depends on the input laser intensity. We experimentally verify that the reflected microwave phase of the optoelectronic metasurface decreases as the incident laser power becomes large, providing a distinct strategy to control the vector fields by the power intensity. Our results offer fundamentally new understanding of the metasurface capabilities and the wave-matter interactions in hybrid materials.
Signal conversion plays an important role in many applications such as communication, sensing, and imaging. Realizing signal conversion between optical and microwave frequencies is a crucial step to construct hybrid communication systems that combine both optical and microwave wireless technologies to achieve better features, which are highly desirable in the future wireless communications. However, such a signal conversion process typically requires a complicated relay to perform multiple operations, which will consume additional hardware/time/energy resources. Here, we report a light-to-microwave transmitter based on the time-varying and programmable metasurface integrated with a high-speed photoelectric detection circuit into a hybrid. Such a transmitter can convert a light intensity signal to two microwave binary frequency shift keying signals by using the dispersion characteristics of the metasurface to implement the frequency division multiplexing. To illustrate the metasurface-based transmitter, a hybrid wireless communication system that allows dual-channel data transmissions in a light-to-microwave link is demonstrated, and the experimental results show that two different videos can be transmitted and received simultaneously and independently. Our metasurface-enabled signal conversion solution may enrich the functionalities of metasurfaces, and could also stimulate new information-oriented applications.
The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing unable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.
Error correction codes, such as low-density parity check (LDPC) codes, are required to be larger scale to meet the increasing demands for reliable and massive data transmission. However, the construction of such a large-scale decoder will result in high complexity and hinder its silicon implementation. Thanks to the advantages of natural computing in high parallelism and low power, we propose a method to synthesize a uniform molecular LDPC decoder by implementing the belief-propagation algorithm with chemical reaction networks (CRNs). This method enables us to flexibly design the LDPC decoder with arbitrary code length, code rate, and node degrees. Compared with existing methods, our proposal reduces the number of reactions to update the variable nodes by 42.86% and the check nodes by 47.37%. Numerical results are presented to show the feasibility and validity of our proposal.
Silicon , Algorithms , Kinetics
Quantum teleportation has significant meaning in quantum information. In particular, entangled states can also be used for perfectly teleporting the quantum state with some probability. This is more practical and efficient in practice. In this paper, we propose schemes to use non-symmetric quantum channel combinations for probabilistic teleportation of an arbitrary two-qubit quantum state from sender to receiver. The non-symmetric quantum channel is composed of a two-qubit partially entangled state and a three-qubit partially entangled state, where partially entangled Greenberger-Horne-Zeilinger (GHZ) state and W state are considered, respectively. All schemes are presented in detail and the unitary operations required are given in concise formulas. Methods are provided for reducing classical communication cost and combining operations to simplify the manipulation. Moreover, our schemes are flexible and applicable in different situations.
