Quantum Logical Gates with Linear Quadripartite Cluster States of Continuous Variables

The concrete schemes to realize three types of basic quantum logical gates using linear quadripartite cluster states of optical continuous variables are proposed. The influences of noises and finite squeezing on the computation precision are analyzed in terms of the fidelity of propagated quantum information through the continuous variable cluster states. The proposed schemes provide direct references for the design of experimental systems of one-way quantum computer based on the cluster entanglement of amplitude and phase quadratures of light.Comment: accepted for publication by PR

MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer

Lidar point clouds, as a type of data with accurate distance perception, can effectively represent the motion and posture of objects in three-dimensional space. However, the sparsity and disorderliness of point clouds make it challenging to extract features directly from them. Many studies have addressed this issue by transforming point clouds into regular voxel representations. However, these methods often lead to the loss of fine-grained local feature information due to downsampling. Moreover, the sparsity of point clouds poses difficulties in efficiently aggregating features in 3D feature layer using voxel-based two-stage methods. To address these issues, this paper proposes a two-stage 3D detection framework called MS$^{2}$3D. In MS$^{2}$3D, we utilize small-sized voxels to extract fine-grained local features and large-sized voxels to capture long-range local features. Additionally, we propose a method for constructing 3D feature layer using multi-scale semantic feature points, enabling the transformation of sparse 3D feature layer into more compact representations. Furthermore, we compute the offset between feature points in the 3D feature layer and the centroid of objects, aiming to bring them as close as possible to the object's center. It significantly enhances the efficiency of feature aggregation. To validate the effectiveness of our method, we evaluated our method on the KITTI dataset and ONCE dataset together

Toward demonstrating controlled-X operation based on continuous variable four-partite cluster state and quantum teleporters

One-way quantum computation based on measurement and multipartite cluster entanglement offers the ability to perform a variety of unitary operations only through different choices of measurement bases. Here we present an experimental study toward demonstrating the controlled-X operation, a two-mode gate, in which continuous variable (CV) four-partite cluster states of optical modes are utilized. Two quantum teleportation elements are used for achieving the gate operation of the quantum state transformation from input target and control states to output states. By means of the optical cluster state prepared off-line, the homodyne detection and electronic feeding forward, the information carried by the input control state is transformed to the output target state. The presented scheme of the controlled-X operation based on teleportation can be implemented nonlocally and deterministically. The distortion of the quantum information resulting from the imperfect cluster entanglement is estimated with the fidelity

PV-SSD: A Projection and Voxel-based Double Branch Single-Stage 3D Object Detector

LIDAR-based 3D object detection and classification is crucial for autonomous driving. However, inference in real-time from extremely sparse 3D data poses a formidable challenge. To address this issue, a common approach is to project point clouds onto a bird's-eye or perspective view, effectively converting them into an image-like data format. However, this excessive compression of point cloud data often leads to the loss of information. This paper proposes a 3D object detector based on voxel and projection double branch feature extraction (PV-SSD) to address the problem of information loss. We add voxel features input containing rich local semantic information, which is fully fused with the projected features in the feature extraction stage to reduce the local information loss caused by projection. A good performance is achieved compared to the previous work. In addition, this paper makes the following contributions: 1) a voxel feature extraction method with variable receptive fields is proposed; 2) a feature point sampling method by weight sampling is used to filter out the feature points that are more conducive to the detection task; 3) the MSSFA module is proposed based on the SSFA module. To verify the effectiveness of our method, we designed comparison experiments

Frequency Conversion of Entangled State

The quantum characteristics of sum-frequency process in an optical cavity with an input signal optical beam, which is a half of entangled optical beams, are analyzed. The calculated results show that the quantum properties of the signal beam can be maintained after its frequency is conversed during the intracavity nonlinear optical interaction. The frequency-conversed output signal beam is still in an entangled state with the retained other half of initial entangled beams. The resultant quantum correlation spectra and the parametric dependences of the correlations on the initial squeezing factor, the optical losses and the pump power of the sum-frequency cavity are calculated. The proposed system for the frequency conversion of entangled state can be used in quantum communication network and the calculated results can provide direct references for the design of experimental systems.Comment: Accepted by Phys. Rev.

Experimental generation of genuine four-partite entangled states with total three-party correlation for continuous variables

We experimentally prepare a new type of continuous variable genuine four-partite entangled states, the quantum correlation property of which is different from that of the four-mode GHZ and cluster states, and which has not any qubit counterpart to be proposed at present. In the criterion inequalities for the full inseparability of the genuine four-partite entangled states, the amplitude and phase quadrature correlation variances totally consisting of three-party combination from the four entangled modes are involved. The measured correlation variances among the quadratures of the prepared entangled states satisfy the sufficient requirements for the full inseparability. The type of entangled states has especially potential application in quantum information with continuous quantum variables

Experimental Demonstration of Quantum Entanglement Between Frequency-Nondegenerate Optical Twin Beams

The quantum entanglement of amplitude and phase quadratures between two intense optical beams with the total intensity of 22mW and the frequency difference of 1nm, which are produced from an optical parametric oscillator operating above threshold, is experimentally demonstrated with two sets of unbalanced Mach-Zehnder interferometers. The measured quantum correlations of intensity and phase are in reasonable agreement with the results calculated based on a semi-classical analysis of the noise characteristics given by C. Fabre et al.Comment: Accepted in Opt. Let

Attention-Aware Adversarial Network for Person Re-Identification

Person re-identification (re-ID) is a fundamental problem in the field of computer vision. The performance of deep learning-based person re-ID models suffers from a lack of training data. In this work, we introduce a novel image-specific data augmentation method on the feature map level to enforce feature diversity in the network. Furthermore, an attention assignment mechanism is proposed to enforce that the person re-ID classifier focuses on nearly all important regions of the input person image. To achieve this, a three-stage framework is proposed. First, a baseline classification network is trained for person re-ID. Second, an attention assignment network is proposed based on the baseline network, in which the attention module learns to suppress the response of the current detected regions and re-assign attentions to other important locations. By this means, multiple important regions for classification are highlighted by the attention map. Finally, the attention map is integrated in the attention-aware adversarial network (AAA-Net), which generates high-performance classification results with an adversarial training strategy. We evaluate the proposed method on two large-scale benchmark datasets, including Market1501 and DukeMTMC-reID. Experimental results show that our algorithm performs favorably against the state-of-the-art methods