arXiv subjects

Zhiling Wang

Publications and source records attributed to Zhiling Wang.

26 records · Page 2Linked to original sources

arXiv2022

An ultra-high gain single-photon transistor in the microwave regime

A photonic transistor that can switch or amplify an optical signal with a single gate photon requires strong non-linear interaction at the single-photon level. Circuit quantum electrodynamics provides great flexibility to generate such an interaction, and thus could serve as an effective platform to realize a high-performance single-photon transistor. Here we demonstrate such a photonic transistor in the microwave regime. Our device consists of two microwave cavities dispersively coupled to a superconducting qubit. A single gate photon imprints a phase shift on the qubit state through one cavity, and further shifts the resonance frequency of the other cavity. In this way, we realize a gain of the transistor up to 53.4 dB, with an extinction ratio better than 20 dB. Our device outperforms previous devices in the optical regime by several orders in terms of optical gain, which indicates a great potential for application in the field of microwave quantum photonics and quantum information processing.

quant-ph↗

arXiv2022

Experimental preparation of generalized cat states for itinerant microwave photons

Generalized cat states represent arbitrary superpositions of coherent states, which are of great importance in various quantum information processing protocols. Here we demonstrate a versatile approach to creating generalized itinerant cat states in the microwave domain, by reflecting coherent state photons from a microwave cavity containing a superconducting qubit. We show that, with a coherent control of the qubit state, a full control over the coherent state superposition can be realized. The prepared cat states are verified through quantum state tomography of the qubit state dependent reflection photon field. We further quantify quantum coherence in the prepared cat states based on the resource theory, revealing a good experimental control on the coherent state superpositions. The photon number statistic and the squeezing properties are also analyzed. Remarkably, fourth-order squeezing is observed in the experimental states. Those results open up new possibilities of applying generalized cat states for the purpose of quantum information processing.

quant-ph↗

arXiv2022

A flying Schrödinger cat in multipartite entangled states

Schrödinger's cat originates from the famous thought experiment querying the counterintuitive quantum superposition of macroscopic objects. As a natural extension, several "cats" (quasi-classical objects) can be prepared into coherent quantum superposition states, which is known as multipartite cat states demonstrating quantum entanglement among macroscopically distinct objects. Here we present a highly scalable approach to deterministically create flying multipartite Schrödinger cat states, by reflecting coherent state photons from a microwave cavity containing a superconducting qubit. We perform full quantum state tomography on the cat states with up to four photonic modes and confirm the existence of quantum entanglement among them. We also witness the hybrid entanglement between discrete-variable states (the qubit) and continuous-variable states (the flying multipartite cat) through a joint quantum state tomography. Our work demonstrates an important experimental control method in the microwave region and provides an enabling step for implementing a series of quantum metrology and quantum information processing protocols based on cat states.

quant-ph↗

arXiv2021

A New Adaptive Noise Covariance Matrices Estimation and Filtering Method: Application to Multi-Object Tracking

Kalman filters are widely used for object tracking, where process and measurement noise are usually considered accurately known and constant. However, the exact known and constant assumptions do not always hold in practice. For example, when lidar is used to track noncooperative targets, the measurement noise is different under different distances and weather conditions. In addition, the process noise changes with the object's motion state, especially when the tracking object is a pedestrian, and the process noise changes more frequently. This paper proposes a new estimation-calibration-correction closed-loop estimation method to estimate the Kalman filter process and measurement noise covariance matrices online. First, we decompose the noise covariance matrix into an element distribution matrix and noise intensity and improve the Sage filter to estimate the element distribution matrix. Second, we propose a calibration method to accurately diagnose the noise intensity deviation. We then propose a correct method to adaptively correct the noise intensity online. Third, under the assumption that the system is detectable, the unbiased and convergence of the proposed method is mathematically proven. Simulation results prove the effectiveness and reliability of the proposed method. Finally, we apply the proposed method to multiobject tracking of lidar and evaluate it on the official KITTI server. The proposed method on the KITTI pedestrian multiobject tracking leaderboard (http://www.cvlibs.net/datasets /kitti/eval_tracking.php) surpasses all existing methods using lidar, proving the feasibility of the method in practical applications. This work provides a new way to improve the performance of the Kalman filter and multiobject tracking.

cs.CV↗

arXiv2021

On-Demand Storage and Retrieval of Microwave Photons Using a Superconducting Multiresonator Quantum Memory

A quantum memory that can store quantum states faithfully and retrieve them on demand has wide applications in quantum information science. An efficient quantum memory in the microwave regime working alongside quantum processors based on superconducting quantum circuits may serve as an important architecture for quantum computers. Here we realize on-demand storage and retrieval of weak coherent microwave photon pulses at the single-photon level. We implement a superconducting multi-resonator quantum memory which is composed of a set of frequency-tunable coplanar transmission line (CPW) resonators. By dynamically tuning the resonant frequencies of the resonators, we achieve tunable memory bandwidth from 10 MHz to 55 MHz, with an overall storage efficiency up to 12 % with well preserved phase coherence. We further demonstrate on-demand storage and retrieval of a time-bin flying qubit. This result opens up a prospect to integrate our chip-based quantum memory with the state-of-the-art superconducting quantum circuit technology for quantum information processing.

quant-ph↗

arXiv2021

Improved superconducting qubit state readout by path interference

High fidelity single shot qubit state readout is essential for many quantum information processing protocols. In superconducting quantum circuit, the qubit state is usually determined by detecting the dispersive frequency shift of a microwave cavity from either transmission or reflection. In this paper, we demonstrate the use of constructive interference between the transmitted and reflected signal to optimize the qubit state readout, with which we find a better resolved state discrimination and an improved qubit readout fidelity. As a simple and convenient approach, our scheme can be combined with other qubit readout methods based on the discrimination of cavity photon states to further improve the qubit state readout.

quant-ph↗

arXiv2021

A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field

Ground segmentation is an important preprocessing task for autonomous vehicles (AVs) with 3D LiDARs. To solve the problem of existing ground segmentation methods being very difficult to balance accuracy and computational complexity, a fast point cloud ground segmentation approach based on a coarse-to-fine Markov random field (MRF) method is proposed. The method uses an improved elevation map for ground coarse segmentation, and then uses spatiotemporal adjacent points to optimize the segmentation results. The processed point cloud is classified into high-confidence obstacle points, ground points, and unknown classification points to initialize an MRF model. The graph cut method is then used to solve the model to achieve fine segmentation. Experiments on datasets showed that our method improves on other algorithms in terms of ground segmentation accuracy and is faster than other graph-based algorithms, which require only a single core of an I7-3770 CPU to process a frame of Velodyne HDL-64E data (in 39.77 ms, on average). Field tests were also conducted to demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2020

ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network

Food recognition has received more and more attention in the multimedia community for its various real-world applications, such as diet management and self-service restaurants. A large-scale ontology of food images is urgently needed for developing advanced large-scale food recognition algorithms, as well as for providing the benchmark dataset for such algorithms. To encourage further progress in food recognition, we introduce the dataset ISIA Food- 500 with 500 categories from the list in the Wikipedia and 399,726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume. Furthermore, we propose a stacked global-local attention network, which consists of two sub-networks for food recognition. One subnetwork first utilizes hybrid spatial-channel attention to extract more discriminative features, and then aggregates these multi-scale discriminative features from multiple layers into global-level representation (e.g., texture and shape information about food). The other one generates attentional regions (e.g., ingredient relevant regions) from different regions via cascaded spatial transformers, and further aggregates these multi-scale regional features from different layers into local-level representation. These two types of features are finally fused as comprehensive representation for food recognition. Extensive experiments on ISIA Food-500 and other two popular benchmark datasets demonstrate the effectiveness of our proposed method, and thus can be considered as one strong baseline. The dataset, code and models can be found at http://123.57.42.89/FoodComputing-Dataset/ISIA-Food500.html.

cs.CV↗