arXiv subjects

Yinqi Chen

Publications and source records attributed to Yinqi Chen.

9 recordsLinked to original sources

arXiv2025

Finite-momentum superconductivity from chiral bands in twisted MoTe$_2$

A recent experiment has reported unconventional superconductivity in twisted bilayer MoTe$_2$, emerging from a normal state that exhibits a finite anomalous Hall effect -- a signature of intrinsic chirality. Motivated by this discovery, we construct a continuum model for twisted MoTe$_2$ constrained by lattice symmetries from first-principles calculations that captures the moir\'{e}-induced inversion symmetry breaking even in the absence of a displacement field. Building on this model, we show that repulsive interactions give rise to finite-momentum superconductivity via the Kohn-Luttinger mechanism in this chiral moir\'{e} system. Remarkably, the finite-momentum superconducting state can arise solely from internal symmetry breaking of the moir\'{e} superlattice, differentiating it from previously studied cases that require external fields. It further features a nonreciprocal quasiparticle dispersion and an intrinsic superconducting diode effect. Our results highlight a novel route to unconventional superconducting states in twisted transition metal dichalcogenides moir\'{e} systems, driven entirely by intrinsic symmetry-breaking effects.

cond-mat.supr-con↗

arXiv2025

Intrinsic superconducting diode effect and nonreciprocal superconductivity in rhombohedral graphene multilayers

Recent experiments have revealed that superconductivity in rhombohedral tetralayer graphene can emerge from a valley-polarized and, hence, chiral normal state. The interplay of pairing and the reduced normal-state symmetries sparked widespread interest. In this work, we demonstrate within a microscopic theoretical formalism that this stabilizes a non-reciprocal superconducting state. Our results are based on a fully self-consistent framework for determining the superconducting order parameter from a Kohn-Luttinger mechanism. We show that the system displays a sizeable intrinsic superconducting diode effect, i.e., without the need for applying external magnetic fields, which is also highly tunable by an external displacement field. Moreover, we find that the angular dependence of the critical current is highly sensitive to the Fermi surfaces of the normal state. Hence, our results suggest that the critical current could provide insights into the type of Fermi surface topology from which superconductivity arises.

cond-mat.supr-con↗

arXiv2025

SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments

Accurate perception of dynamic traffic scenes is crucial for high-level autonomous driving systems, requiring robust object motion estimation and instance segmentation. However, traditional methods often treat them as separate tasks, leading to suboptimal performance, spatio-temporal inconsistencies, and inefficiency in complex scenarios due to the absence of information sharing. This paper proposes a multi-task SemanticFlow framework to simultaneously predict scene flow and instance segmentation of full-resolution point clouds. The novelty of this work is threefold: 1) developing a coarse-to-fine prediction based multi-task scheme, where an initial coarse segmentation of static backgrounds and dynamic objects is used to provide contextual information for refining motion and semantic information through a shared feature processing module; 2) developing a set of loss functions to enhance the performance of scene flow estimation and instance segmentation, while can help ensure spatial and temporal consistency of both static and dynamic objects within traffic scenes; 3) developing a self-supervised learning scheme, which utilizes coarse segmentation to detect rigid objects and compute their transformation matrices between sequential frames, enabling the generation of self-supervised labels. The proposed framework is validated on the Argoverse and Waymo datasets, demonstrating superior performance in instance segmentation accuracy, scene flow estimation, and computational efficiency, establishing a new benchmark for self-supervised methods in dynamic scene understanding.

cs.CV↗

arXiv2025

SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios

Vehicle detection and localization in complex traffic scenarios pose significant challenges due to the interference of moving objects. Traditional methods often rely on outlier exclusions or semantic segmentations, which suffer from low computational efficiency and accuracy. The proposed SSF-PAN can achieve the functionalities of LiDAR point cloud based object detection/localization and SLAM (Simultaneous Localization and Mapping) with high computational efficiency and accuracy, enabling map-free navigation frameworks. The novelty of this work is threefold: 1) developing a neural network which can achieve segmentation among static and dynamic objects within the scene flows with different motion features, that is, semantic scene flow (SSF); 2) developing an iterative framework which can further optimize the quality of input scene flows and output segmentation results; 3) developing a scene flow-based navigation platform which can test the performance of the SSF perception system in the simulation environment. The proposed SSF-PAN method is validated using the SUScape-CARLA and the KITTI datasets, as well as on the CARLA simulator. Experimental results demonstrate that the proposed approach outperforms traditional methods in terms of scene flow computation accuracy, moving object detection accuracy, computational efficiency, and autonomous navigation effectiveness.

cs.RO↗

arXiv2024

24 days-stable CNOT-gate on fluxonium qubits with over 99.9% fidelity

Fluxonium qubit is a promising building block for quantum information processing due to its long coherence time and strong anharmonicity. In this paper, we realize a 60 ns direct CNOT-gate on two inductively-coupled fluxonium qubits using selective darkening approach, resulting in a gate fidelity as high as 99.94%. The fidelity remains above 99.9% for 24 days without any recalibration between randomized benchmarking measurements. Compared with the 99.96% fidelity of a 60 ns identity gate, our data brings the investigation of the non-decoherence-related errors during gate operations down to $2 \times 10^{-4}$. The present result adds a simple and robust two-qubit gate into the still relatively small family of "the beyond three nines" demonstrations on superconducting qubits.

quant-ph↗

arXiv2024

Verifying the analogy between transversely coupled spin-1/2 systems and inductively-coupled fluxoniums

We report a detailed characterization of two inductively coupled superconducting fluxonium qubits for implementing high-fidelity cross-resonance gates. Our circuit stands out because it behaves very closely to the case of two transversely coupled spin-1/2 systems. In particular, the generally unwanted static ZZ-term due to the non-computational transitions is nearly absent despite a strong qubit-qubit hybridization. Spectroscopy of the non-computational transitions reveals a spurious LC-mode arising from the combination of the coupling inductance and the capacitive links between the terminals of the two qubit circuits. Such a mode has a minor effect on our specific device, but it must be carefully considered for optimizing future designs.

quant-ph↗

arXiv2024

BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data

Compared with real-time multi-object tracking (MOT), offline multi-object tracking (OMOT) has the advantages to perform 2D-3D detection fusion, erroneous link correction, and full track optimization but has to deal with the challenges from bounding box misalignment and track evaluation, editing, and refinement. This paper proposes "BiTrack", a 3D OMOT framework that includes modules of 2D-3D detection fusion, initial trajectory generation, and bidirectional trajectory re-optimization to achieve optimal tracking results from camera-LiDAR data. The novelty of this paper includes threefold: (1) development of a point-level object registration technique that employs a density-based similarity metric to achieve accurate fusion of 2D-3D detection results; (2) development of a set of data association and track management skills that utilizes a vertex-based similarity metric as well as false alarm rejection and track recovery mechanisms to generate reliable bidirectional object trajectories; (3) development of a trajectory re-optimization scheme that re-organizes track fragments of different fidelities in a greedy fashion, as well as refines each trajectory with completion and smoothing techniques. The experiment results on the KITTI dataset demonstrate that BiTrack achieves the state-of-the-art performance for 3D OMOT tasks in terms of accuracy and efficiency.

cs.CV↗

arXiv2023

Voltage Activated Parametric Entangling Gates on Gatemons

We describe the generation of entangling gates on superconductor-semiconductor hybrid qubits by ac voltage modulation of the Josephson energy. Our numerical simulations demonstrate that the unitary error can be below $10^{-5}$ in a variety of 75-ns-long two-qubit gates (CZ, $i$SWAP, and $\sqrt{i\mathrm{SWAP}}$) implemented using parametric resonance. We analyze the conditional ZZ phase and demonstrate that the CZ gate needs no further phase correction steps, while the ZZ phase error in SWAP-type gates can be compensated by choosing pulse parameters. With decoherence considered, we estimate that qubit relaxation time needs to exceed $70μ\mathrm{s}$ to achieve the 99.9% fidelity threshold.

quant-ph↗

arXiv2022

Fast Flux Entangling Gate for Fluxonium Circuits

We analyze a high-fidelity two-qubit gate using fast flux pulses on superconducting fluxonium qubits. The gate is realized by temporarily detuning magnetic flux through fluxonium loop away from the half flux quantum sweet spot. We simulate dynamics of two capacitively coupled fluxoniums during the flux pulses and optimize the pulse parameters to obtain a highly accurate $\sqrt{i\mathrm{swap}}$-like entangling gate. We also evaluate the effect of the flux noise and qubit relaxation on the gate fidelity. Our results demonstrate that the gate error remains below $10^{-4}$ for currently achievable magnitude of the flux noise and qubit relaxation time.

quant-ph↗