arXiv subjects

Dingli Yuan

Publications and source records attributed to Dingli Yuan.

11 recordsLinked to original sources

arXiv2026

Extracting the pairing gap from van Hove singularities in rf spectra of the Fermi Hubbard model

We show that van Hove singularities in rf spectra of the 3D attractive Fermi Hubbard model provide a robust route to extracting the pairing gap. Four types of singularities are classified, and their spectral positions are shown to depend solely on the pairing gap $\Delta$ and chemical potential $\mu$ through simple algebraic relations. Measuring two well-resolved singularities therefore determines both parameters without requiring full spectral fitting. Numerical simulations incorporating phenomenological lifetime and scattering broadenings confirm that these features remain visible in both momentum-integrated and $k_z$-integrated spectra, and become more pronounced at stronger coupling where conventional back-bending methods lose sensitivity. At half filling, particle-hole symmetry fixes $\mu$, reducing the extraction to a single singularity measurement. These results establish vHS analysis as a practical spectroscopic diagnostic for pairing in quantum-simulated 3D Fermi Hubbard systems.

cond-mat.quant-gas↗

arXiv2026

Spectral study of the pseudogap in unitary Fermi gases

The existence of a pseudogap in unitary Fermi gases has recently been established and measured experimentally [Li et al., Nature 626, 288 (2024)]. This lends strong support for the pairing origin as the mechanism of the pseudogap in Fermi superfluids. Here we present a spectral study of unitary Fermi gases, and show how the data can be understood quantitatively, when compared with theoretically calculated momentum-resolved rf or microwave spectra, and the pseudogap extracted from the spectra. We use an iterative treatment of the fermion self energy and hence the spectral function, beyond previous pseudogap approximation, based on a pairing fluctuation theory that incorporates both particle-particle and particle-hole T matrices, with self-consistent self energy feedback. Our results not only provide a microscopic explanation of the experimental data but also strengthen the support for both the pairing-induced pseudogap physics and the pairing fluctuation theory of Fermi superfluidity.

cond-mat.quant-gas↗

arXiv2026

Rf spectra and pseudogap in ultracold Fermi gases across the BCS-BEC crossover from pairing fluctuation theory

The pseudogap phenomenon is a hallmark of strongly interacting Fermi systems, from high-temperature superconductors to ultracold atomic gases, yet its precise origin remains debated. Here we calculate the spectral function and rf spectra of ultracold atomic gases across the BCS-BEC crossover to quantitatively investigate the pairing mechanism of the pseudogap. We advance our pairing fluctuation theory by incorporating particle-hole fluctuations, which renormalize the effective interaction in the particle-particle channel. To achieve quantitative accuracy, we employ a full numerical convolution for the pair susceptibility and self-energy, moving beyond previous analytic pseudogap approximations. This convolution approach automatically captures two critical effects: (i) the full spectral broadening of fermions due to finite pair lifetime, and (ii) the previously neglected pair-hole scattering effect, which manifests as a substantial Hartree energy. We calculate the spectral function, and use rf spectral intensity maps and energy distribution curves to determine the quasiparticle dispersion. From these, we extract the pseudogap $Δ$, Hartree energy, and chemical potential, mapping their evolution across the crossover. Our results show that the pseudogap emerges continuously as the system moves from the BCS regime toward BEC. Furthermore, the pair spectral function reveals that pairs become diffusive at energies above 2$Δ$, indicating that the pair lifetime is governed by virtual binding and unbinding processes. Our calculations achieve quantitative agreement with recent experiments across the BCS-BEC crossover, including at unitarity, providing strong support for a pairing-based origin of the pseudogap as described by our pairing fluctuation theory.

cond-mat.quant-gas↗

arXiv2026

Effects of particle-hole fluctuations on the superfluid transition in two-dimensional atomic Fermi gases

Proper treatment of the many-body interactions is of paramount importance in our understanding of strongly correlated systems. Here we investigate the effects of particle-hole fluctuations on the Berezinskii-Kosterlitz-Thouless (BKT) transition in two-dimensional Fermi gases throughout the entire BCS-BEC crossover. We include self-consistently in the self energy treatment the entire particle-hole $T$ matrix, which constitutes a renormalization of the bare interaction that appears in the particle-particle scattering $T$ matrix, leading to a screening of the pairing interaction and hence a dramatic reduction of the pairing gap and the transition temperature. The BKT transition temperature $T_\text{BKT}$ is determined by the critical phase space density, for which the pair density and pair mass are determined using a pairing fluctuation theory, which accommodates self-consistently the important self-energy feedback in the treatment of finite-momentum pairing fluctuations. The screening strength varies continuously from its maximum in the BCS limit to essentially zero in BEC limit. In the unitary regime, it leads to an interaction-dependent shift of $T_\text{BKT}$ towards the BEC regime. This shift is crucial in an attempt to explain experimental data quantitatively, which often depends on the interaction strength. Our findings are consistent with available experimental results in the unitary and BEC regimes and with quantum Monte Carlo simulations in the BCS and unitary regimes.

cond-mat.quant-gas↗

arXiv2025

AutoMAS: A Generic Multi-Agent System for Algorithm Self-Adaptation in Wireless Networks

The wireless communication environment has the characteristic of strong dynamics. Conventional wireless networks operate based on the static rules with predefined algorithms, lacking the self-adaptation ability. The rapid development of artificial intelligence (AI) provides a possibility for wireless networks to become more intelligent and fully automated. As such, we plan to integrate the cognitive capability and high intelligence of the emerging AI agents into wireless networks. In this work, we propose AutoMAS, a generic multi-agent system which can autonomously select the most suitable wireless optimization algorithm according to the dynamic wireless environment. Our AutoMAS combines theoretically guaranteed wireless algorithms with agents' perception ability, thereby providing sounder solutions to complex tasks no matter how the environment changes. As an example, we conduct a case study on the classical channel estimation problem, where the mobile user moves in diverse environments with different channel propagation characteristics. Simulation results demonstrate that our AutoMAS can guarantee the highest accuracy in changing scenarios. Similarly, our AutoMAS can be generalized to autonomously handle various tasks in 6G wireless networks with high accuracy.

eess.SP↗

arXiv2025

WMAS: A Multi-Agent System Towards Intelligent and Customized Wireless Networks

The fast development of Artificial Intelligence (AI) agents provides a promising way for the realization of intelligent and customized wireless networks. In this paper, we propose a Wireless Multi-Agent System (WMAS), which can provide intelligent and customized services for different user equipment (UEs). Note that orchestrating multiple agents carries the risk of malfunction, and multi-agent conversations may fall into infinite loops. It is thus crucial to design a conversation topology for WMAS that enables agents to complete UE task requests with high accuracy and low conversation overhead. To address this issue, we model the multi-agent conversation topology as a directed acyclic graph and propose a reinforcement learning-based algorithm to optimize the adjacency matrix of this graph. As such, WMAS is capable of generating and self-optimizing multi-agent conversation topologies, enabling agents to effectively and collaboratively handle a variety of task requests from UEs. Simulation results across various task types demonstrate that WMAS can achieve higher task performance and lower conversation overhead compared to existing multi-agent systems. These results validate the potential of WMAS to enhance the intelligence of future wireless networks.

cs.MA↗

arXiv2025

A Peaceman-Rachford Splitting Approach with Deep Equilibrium Network for Channel Estimation

Multiple-input multiple-output (MIMO) is pivotal for wireless systems, yet its high-dimensional, stochastic channel poses significant challenges for accurate estimation, highlighting the critical need for robust estimation techniques. In this paper, we introduce a novel channel estimation method for the MIMO system. The main idea is to construct a fixed-point equation for channel estimation, which can be implemented into the deep equilibrium (DEQ) model with a fixed network. Specifically, the Peaceman-Rachford (PR) splitting method is applied to the dual form of the regularized minimization problem to construct fixed-point equation with non-expansive property. Then, the fixed-point equation is implemented into the DEQ model with a fixed layer, leveraging its advantage of the low training complexity. Moreover, we provide a rigorous theoretical analysis, demonstrating the convergence and optimality of our approach. Additionally, simulations of hybrid far- and near-field channels demonstrate that our approach yields favorable results, indicating its ability to advance channel estimation in MIMO system.

eess.SP↗

arXiv2024

Multiplexed Streaming Codes for Messages with Different Decoding Delays in Channel with Burst and Random Erasures

In a real-time transmission scenario, messages are transmitted through a channel that is subject to packet loss. The destination must recover the messages within the required deadline. In this paper, we consider a setup where two different types of messages with distinct decoding deadlines are transmitted through a channel model that introduces either one burst erasure of length at most $B$, or $N$ random erasures in any fixed-sized sliding window. The message with a short decoding deadline $T_{\mathrm{u}}$ is referred to as an urgent message, while the other one with a decoding deadline $T_{\mathrm{v}}$ ($T_{\mathrm{v}} > T_{\mathrm{u}}$) is referred to as a less urgent message. We consider the scenario where $T_{\mathrm{v}} > T_{\mathrm{u}} + B$ and propose a non-trivial achievable region $\mathcal{R}$ for the aforementioned channel model. We propose a novel merging approach to encode two message streams of different urgency levels into a single flow and present explicit constructions for encoding, contributing to the establishment of the achievability of region $\mathcal{R}$. Our comprehensive analysis demonstrates that this region encompasses the rate pairs of existing encoding schemes and coincides with the capacity region in burst channel scenarios. Lastly, we investigate the property of the achievable region $\mathcal{R}$, proving that it is the largest one obtained from all the rate pairs under the merging method.

cs.IT↗

arXiv2024

Quantum Geometry, Anomalous Scaling, and Strong Pseudogap Superfluidity in a Flat-Band Lieb Lattice

Flat-band systems such as magic-angle twisted bilayer graphene host strong-correlation superconductivity at vanishingly weak coupling, yet how quantum geometry and pairing fluctuations conspire to drive this phenomenon remains an open question. We investigate finite-temperature superfluidity in a quasi-two-dimensional Lieb lattice using a pairing fluctuation theory with a band-uniform attractive interaction $g<0$ that isolates the intrinsic quantum geometric contributions. Quantum geometry significantly amplifies superfluidity; the geometric pair hopping integral surpasses its conventional counterpart, and the geometric superfluid density becomes the dominant in-plane transport component. When the Fermi level enters the flat band, the BCS paradigm breaks down entirely; the pairing gap and $T_\text{c}$ shift from exponential to anomalous power-law scaling $\Delta, T_\text{c} \propto |g|^\nu$ ($\nu>1$), and the superfluid density inherits an unconventional power-law temperature dependence at low temperatures. In the 2D limit ($t_z=0$), the pseudogap at $T_\text{c}$ nearly saturates the zero-temperature gap even at $|g|/t=0.001$, placing the system in a strong-pseudogap regime that would otherwise require unitary or BEC-scale interactions. These findings establish a microscopic mechanism for flat-band enhanced superfluidity and offer testable predictions for ultracold atom experiments.

cond-mat.quant-gas↗

arXiv2023

Distributed Matrix Multiplication with a Smaller Recovery Threshold through Modulo-based Approaches

This paper considers the problem of calculating the matrix multiplication of two massive matrices $\mathbf{A}$ and $\mathbf{B}$ distributedly. We provide a modulo technique that can be applied to coded distributed matrix multiplication problems to reduce the recovery threshold. This technique exploits the special structure of interpolation points and can be applied to many existing coded matrix designs. Recently studied discrete Fourier transform based code achieves a smaller recovery threshold than the optimal MatDot code with the expense that it cannot resist stragglers. We also propose a distributed matrix multiplication scheme based on the idea of locally repairable code to reduce the recovery threshold of MatDot code and provide resilience to stragglers. We also apply our constructions to a type of matrix computing problems, where generalized linear models act as a special case.

cs.IT↗

arXiv2023

Privacy-Preserving Polynomial Computing Over Distributed Data

In this letter, we delve into a scenario where a user aims to compute polynomial functions using their own data as well as data obtained from distributed sources. To accomplish this, the user enlists the assistance of $N$ distributed workers, thereby defining a problem we refer to as privacy-preserving polynomial computing over distributed data. To address this challenge, we propose an approach founded upon Lagrange encoding. Our method not only possesses the ability to withstand the presence of stragglers and byzantine workers but also ensures the preservation of security. Specifically, even if a coalition of $X$ workers collude, they are unable to acquire any knowledge pertaining to the data originating from the distributed sources or the user.

cs.CR↗