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Increasing the quantum information processing power with limited number of hosts is vital for achieving quantum advantage. Here we propose a novel scheme that achieves a scalable n-ion-2n-qubit quantum processor utilizing four internal levels of each ion, and experimentally implement a 1-ion-2-qubit universal processor using the valence electron spin and nuclear spin of a single 171Yb+ ion. Fidelities of single-qubit and two-qubit gates are around 0.98 obtained by quantum process tomography. Additionally, the Grover's algorithm is implemented with a successful rate exceeding 0.99. We provide explicit scaling-up protocols based on standard laser-less and laser-based frameworks, and further demonstrate that the electron/nuclear-spin scheme allows less demanding two-qubit entangling gates between different ions. The replacement of some inter-atomic gates by intra-atomic gates could increase the fidelity of some quantum circuits. Our work paves the way towards achieving 2n-times increase in the size of quantum computational Hilbert space with n ions.

Under the genuinely nonlinear assumptions for one dimensional $n\times n$ strictly hyperbolic conservation laws, we investigate the geometric blowup of smooth solutions and the development of singularities when the small initial data fulfill the generic nondegenerate condition. At first, near the unique blowup point we give a precise description on the space-time blowup rate of the smooth solution and meanwhile derive the cusp singularity structure of characteristic envelope. These results are established through extending the smooth solution of the completely nonlinear blowup system across the blowup time. Subsequently, by utilizing a new form on the resulting 1-D strictly hyperbolic system with $(n-1)$ good components and one bad component, together with the choice of an efficient iterative scheme and some involved analyses, a weak entropy shock wave starting from the blowup point is constructed.

A novel optical method for distinguishing chiral molecules is proposed and validated within a quantum simulator employing a trapped-ion qudit. This approach correlates the sign disparity of the dipole moment of chiral molecules with distinct cyclic evolution trajectories, yielding the unity population contrast induced by the different non-Abelian holonomies corresponding to the chirality. Harnessing the principles of holonomic quantum computation (HQC), our method achieves highly efficient, non-adiabatic, and robust detection and separation of chiral molecules. Demonstrated in a trapped ion quantum simulator, this scheme achieves nearly 100% contrast between the two enantiomers in the population of a specific state, showcasing its resilience to the noise inherent in the driving field.

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios., Comment: 12 pages, 8 figures

Recent advancements in computational power and algorithms have enabled unabridged data (e.g., raw images or audio) to be used as input in some models (e.g., deep learning). However, the black box nature of such models reduces their likelihood of adoption by marketing scholars. Our paradigm of analysis, the Transparent Model of Unabridged Data (TMUD), enables researchers to investigate the inner workings of such black box models by incorporating an ex ante filtration module and an ex post experimentation module. We empirically demonstrate the TMUD by investigating the role of facial components and sexual dimorphism in face perceptions, which have implications for four marketing contexts: advertisement (perceptions of approachability, trustworthiness, and competence), brand (perceptions of whether a face represents a brand's typical customer), category (perceptions of whether a face represents a category's typical customer), and customer persona (perceptions of whether a face represents the persona of a brand's customer segment). Our results reveal new and useful findings that enrich the existing literature on face perception, most of which is based on abridged attributes (e.g., width of mouth). The TMUD has great potential to be a useful paradigm for generating theoretical insights and may encourage more marketing researchers and practitioners to use unabridged data., Comment: 6 figures and 6 tables

A novel matrix pencil-based interference mitigation approach for FMCW radars is proposed in this paper. The interference-contaminated segment of the beat signal is firstly cut out and then the signal samples in the cut-out region are reconstructed by modeling the beat signal as a sum of complex exponentials and using the matrix pencil method to estimate their parameters. The efficiency of the proposed approach for the interference with different parameters (i.e. interference duration, signal-to-noise ratio (SNR), and different target scenarios) is investigated by means of numerical simulations. The proposed interference mitigation approach is intensively verified on experimental data. Comparisons of the proposed approach with the zeroing and other beat-frequency interpolation techniques are presented. The results indicate the broad applicability and superiority of the proposed approach, especially in low SNR and long interference duration situations., Comment: 15 pages, 14 figures

Vector network analyzers (VNAs) have become one of the indispensable tools in various fields, such as medicine, material, geology, communication, and etc, due to the capacity of measuring and analyzing the response of the object under test. Conventional VNAs, commonly based on mixing architecture, have to make compromises among accuracy, dynamic range, and bandwidth. In this paper, we propose a wideband photonic vector network analyzer (PVNA) based on wideband direct photonic digitizing. Ultrastable optical trains directly undersample the response signals from objects under test, followed by electrooptic conversion and quantization, obviating the intricate downconversion procedures in traditional VNAs. Adopting existing commercial devices, the proposed PVNA can not only extend the measurement frequency range to 110 GHz or higher but also achieve a high linearity and accuracy performance. To validate the theoretical analysis, we establish an experimental PVNA, realizing a measurable frequency span of up to 40 GHz and a dynamic range of more than 120 dB. A measured scattering parameters of a bandpass filter The experimental result is well consistent with that of a commercial network analyzer.