arXiv subjects

Yuxi Zhang

Publications and source records attributed to Yuxi Zhang.

24 records · Page 2Linked to original sources

arXiv2022

Photoinduced enhancement of superconductivity in the plaquette Hubbard model

Real-time dynamics techniques have proven increasingly useful in understanding strongly correlated systems both theoretically and experimentally. By employing unbiased time-resolved exact diagonalization, we study pump dynamics in the two-dimensional plaquette Hubbard model, where distinct hopping integrals $t_h$ and $t_h^\prime$ are present within and between plaquettes. In the intermediate coupling regime, a significant enhancement of $d$-wave superconductivity is observed and compared with that obtained by simple examination of expectation values with the eigenstates of the Hamiltonian. Our work provides further understanding of superconductivity in the Hubbard model, extends the description of the pairing amplitude to the frequency-anisotropy plane, and offers a promising approach for experimentally engineering emergent out-of-equilibrium states.

cond-mat.str-el↗

arXiv2022

Effect of Emitters on Quantum State Transfer in Coupled Cavity Arrays

Over the last decade, conditions for perfect state transfer in quantum spin chains have been discovered, and their experimental realizations addressed. In this paper, we consider an extension of such studies to quantum state transfer in a coupled cavity array including the effects of atoms in the cavities which can absorb and emit photons as they propagate down the array. Our model is equivalent to previously examined spin chains in the one-excitation sector and in the absence of emitters. We introduce a Monte Carlo approach to the inverse eigenvalue problem which allows the determination of the inter-cavity and cavity-emitter couplings resulting in near-perfect quantum state transfer fidelity, and examine the time dependent polariton wave function through exact diagonalization of the resulting Tavis-Cummings-Hubbard Hamiltonian. The effect of inhomogeneous emitter locations is also evaluated.

quant-ph↗

arXiv2020

Charge-Density Wave Order on a $π$-flux Square Lattice

The effect of electron-phonon coupling (EPC) on Dirac fermions has recently been explored numerically on a honeycomb lattice, leading to precise quantitative values for the finite temperature and quantum critical points. In this paper, we use the unbiased determinant Quantum Monte Carlo (DQMC) method to study the Holstein model on a half-filled staggered-flux square lattice, and compare with the honeycomb lattice geometry, presenting results for a range of phonon frequencies $0.1 \leqslant ω\leqslant 2.0$. We find that the interactions give rise to charge-density wave (CDW) order, but only above a finite coupling strength $λ_{\rm crit}$. The transition temperature is evaluated and presented in a $T_c$-$λ$ phase diagram. An accompanying mean-field theory (MFT) calculation also predicts the existence of quantum phase transition (QPT), but at a substantially smaller coupling strength.

cond-mat.str-el↗

arXiv2019

Converged Deep Framework Assembling Principled Modules for CS-MRI

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR data acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k-space data. Conventional methods typically optimize an energy function, producing reconstruction of high quality, but their iterative numerical solvers unavoidably bring extremely slow processing. Recent data-driven techniques are able to provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following constraints underlying the regularizers of conventional methods so that the reliability of their reconstruction results are questionable. In this paper, we propose a converged deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering \emph{efficient} and \emph{reliable} reconstruction. We also apply the framework to two practical tasks, \emph{i.e.}, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios.

eess.IV↗

arXiv2019

Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation

Enhancing visual qualities for underexposed images is an extensively concerned task that plays important roles in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we in this work develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to solve the underexposed image correction task. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. Additionally, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What's more, we employ our method to single image haze removal whose experimental results further demonstrate its superiorities.

cs.CV↗

arXiv2018

A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene.

cs.CV↗