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In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This comprehensive first-principles many-body computational toolkit, termed $\texttt{Zen}$, utilizes the Vienna $\textit{ab initio}$ simulation package and the $\texttt{Quantum ESPRESSO}$ code to perform density functional theory calculations and generate band structures for realistic materials. The challenges associated with correlated electron systems are addressed through two distinct yet complementary quantum impurity solvers: the natural orbitals renormalization group solver for zero temperature and the hybridization expansion continuous-time quantum Monte Carlo solver for finite temperature. Additionally, this newly developed toolkit incorporates several valuable post-processing tools, such as $\texttt{ACFlow}$, which employs the maximum entropy method and the stochastic pole expansion method for the analytic continuation of Matsubara Green's functions and self-energy functions. To validate the performance of this toolkit, we examine three representative cases: the correlated metal SrVO$_{3}$, the nickel-based unconventional superconductor La$_{3}$Ni$_{2}$O$_{7}$, and the wide-gap Mott insulator MnO. The results obtained demonstrate strong agreement with experimental findings and previously available theoretical results. Notably, we successfully elucidate the quasiparticle peak and band renormalization in SrVO$_{3}$, the dominance of Hund correlation in La$_{3}$Ni$_{2}$O$_{7}$, and the pressure-driven insulator-metal transition as well as the high-spin to low-spin transition in MnO. These findings suggest that $\texttt{Zen}$ is proficient in accurately describing the electronic structures of $d$-electron correlated materials., Comment: 14 pages, 8 figures, 1 table

The IEEE 802.11 MAC layer utilizes the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism for channel contention and access. However, in densely deployed Wi-Fi scenarios, intense competition may lead to packet collisions among users. Although many studies have used machine learning methods to optimize channel contention and access mechanisms, most of them are based on AP-centric single-agent models or distributed models, which still suffer poor generalization and insensitivity to dynamic environments. To address these challenges, this paper proposes an intelligent channel contention access mechanism that combines Federated Learning (FL) and Deep Deterministic Policy Gradient (DDPG) algorithms. Additionally, an FL model training pruning strategy and weight aggregation algorithm are designed to enhance the effectiveness of training samples and reduce the average MAC delay. We evaluate and validate the proposed solution using NS3-AI framework. Simulation results show that in static scenarios, our proposed scheme reduces the average MAC delay by 25.24% compared to traditional FL algorithms. In dynamic scenarios, it outperforms Average Federated Reinforcement Learning (A-FRL) and distributed Deep Reinforcement Learning (DRL) algorithms by 25.72% and 45.9%, respectively., Comment: submitted to a conference

Low-energy excitations may manifest intricate behaviors of correlated electron systems and provide essential insights into the dynamics of quantum states and phase transitions. We study a two-orbital Hubbard model featuring the so-called holon-doublon low-energy excitations in the Mott insulating narrow band in the orbital-selective Mott phase (OSMP). We employ an improved dynamical mean-field theory (DMFT) technique to calculate the spectral functions at zero temperature. We show that the holon-doublon bound state is not the sole component of the low-energy excitations. Instead, it should be a bound state composed of a Kondo-like state in the wide band and a doublon in the narrow band, named inter-band Kondo-like (IBK) bound states. Notably, as the bandwidths of the two bands approach each other, we find anomalous IBK bound-state excitations in the metallic {\em wide} band., Comment: 5 pages, 4 figures

High-$T_c$ superconductivity (SC) was recently found in the bilayer material La$_3$Ni$_2$O$_7$ (La327) under high pressures. We study the bilayer two-orbital Hubbard model derived from the band structure of the La327. The model is solved by cluster dynamical mean-field theory (CDMFT) with natural orbitals renormalization group (NORG) as impurity solver at zero temperature, considering only normal states. With hole doping, we have observed sequentially the Mott insulator (Mott), pseudogap (PG), non-Fermi liquid (NFL), and Fermi liquid (FL) phases, with quantum correlations decreasing. The ground state of the La327 is in the NFL phase with Hund spin correlation, which transmits the Ni-$3d_{z^2}$ ($z$) orbital inter-layer AFM correlation to the Ni-$3d_{x^2-y^2}$ orbitals. When the $\sigma$-bonding state of the $z$ orbitals ($z+$) is no longer fully filled, the inter-layer antiferromagnetic (AFM) correlations weaken rapidly. At low pressures, the fully filled $z+$ band supports a strong inter-layer AFM correlations, potentially favoring short-range spin density wave (SDW) and suppressing SC. Hole doping at low pressures may achieve a similar effect to high pressures, under which the $z+$ band intersects with the Fermi level, and consequently the spin correlations weaken remarkably, potentially suppressing the possible short-range SDW and favoring SC., Comment: 8 pages, 9 figures

By preforming DFT+DMFT calculations, we systematically investigate the correlated electronic structure in the newly discovered monolayer-trilayer (ML-TL) phase of La$_3$Ni$_2$O$_7$ (1313-La327). Our calculated Fermi surfaces are in good agreement with the angle-resolved photoemission spectroscopy (ARPES) results. We find that 1313-La327 is a multiorbital correlated metal. An orbital-selective Mott behavior is found in ML. The ML Ni-3$d_{z^2}$ orbitals exhibit a Mott behavior, while the ML Ni-3$d_{x^2-y^2}$ orbitals are metallic due to self-doping. And the ML also shows features of heavy fermions, which indicates that there may be Kondo physics in 1313-La327. We also find a large static local spin susceptibility of ML Ni, suggesting that there is large spin fluctuation in 1313-La327. The TL Ni-$e_g$ orbitals possess similar electronic correlation to those in La$_4$Ni$_3$O$_{10}$ (La4310). The $e_g$ orbitals of the outer-layer Ni in TL (TL-outer Ni) show non-Fermi liquid behaviors. Besides, large weight of high-spin states are found in TL-outer Ni and ML Ni, implying Hundness. Under 16 GPa, a Lifshitz transition is revealed by our calculations and a La-related band crosses the Fermi level. Our work provides a theoretical reference for studying other potential mixed-stacked nickelate superconductors., Comment: 7 pages, 3 figures, 3 tables

The tensor network algorithm, a family of prevalent numerical methods for quantum many-body problems, aptly captures the entanglement properties intrinsic to quantum systems, enabling precise representation of quantum states. However, its computational cost is notably high, particularly in calculating physical observables like correlation functions. To surmount the computational challenge and enhance efficiency, we propose integrating the Green's function Monte Carlo (GFMC) method with the projected entangled pair state (PEPS) ansatz. This approach combines the high-efficiency characteristics of Monte Carlo with the sign-free nature of tensor network states and proves effective in addressing the computational bottleneck. To showcase its prowess, we apply this hybrid approach to investigate the antiferromagnetic $J_1$-$J_2$ Heisenberg model on the square lattice, a model notorious for its sign problem in quantum Monte Carlo simulations. Our results reveal a substantial improvement in the accuracy of ground-state energy when utilizing a preliminary PEPS as the guiding wave function for GFMC. By calculating the structure factor and spin-spin correlation functions, we further characterize the phase diagram, identifying a possible columnar valence-bond state phase within the intermediate parameter range of $0.52 < J_2/J_1 < 0.58$. This comprehensive study underscores the efficacy of our combined approach, demonstrating its ability to accurately simulate frustrated quantum spin systems while ensuring computational efficiency., Comment: 11 pages, 15 figures

Generative Diffusion Models (GDMs), have made significant strides in modeling complex data distributions across diverse domains. Meanwhile, Deep Reinforcement Learning (DRL) has demonstrated substantial improvements in optimizing Wi-Fi network performance. Wi-Fi optimization problems are highly challenging to model mathematically, and DRL methods can bypass complex mathematical modeling, while GDMs excel in handling complex data modeling. Therefore, combining DRL with GDMs can mutually enhance their capabilities. The current MAC layer access mechanism in Wi-Fi networks is the Distributed Coordination Function (DCF), which dramatically declines in performance with a high number of terminals. In this paper, we apply diffusion models to deep deterministic policy gradient (DDPG), namely the Deep Diffusion Deterministic Policy (D3PG) algorithm to optimize the Wi-Fi performance. Although similar integrations of reinforcement learning with generative diffusion models have been explored previously, we are the first to apply this approach to Wi-Fi network performance optimization. We propose an access mechanism that jointly adjusts the contention window and aggregation frame length based on the D3PG algorithm. Through simulations, we have demonstrated that this mechanism significantly outperforms existing Wi-Fi standards in dense Wi-Fi scenarios, maintaining performance even as the number of users sharply increases., Comment: This paper has been submitted to WCNC 2025 and is currently under review

High temperature superconductivity was recently found in the bilayer nickelate $\rm{La}_3 \rm{Ni}_2 \rm{O}_7$ (La327), followed by the discovery of superconductivity in the trilayer $\rm{La}_4 \rm{Ni}_3 \rm{O}_{10}$ (La4310), under high pressure. Through studying the electronic correlation of La4310 with DFT+DMFT, and further comparing it with that of La327, we find that the $e_g$ orbitals of the outer-layer Ni cations in La4310 have a similar (but slightly weaker) electronic correlation to those in La327, in which the electrons behave as a non-Fermi liquid with Hund correlation and linear-in-temperature scattering rate. Our results suggest that the experimentally observed ``strange metal'' behavior may be explained by the Hund spin correlation featuring high spin states and spin-orbital separation. In contrast, the electrons in the inner-layer Ni cations in La4310 behave as a Fermi liquid. The weaker electronic correlation in La4310 is attributed to more hole-doping, which may explain its lower superconducting transition temperature., Comment: 6 pages, 4 figures

By means of density functional theory plus dynamical mean-field theory (DFT+DMFT), we investigate the correlated electronic structures of La$_3$Ni$_2$O$_7$ under high pressure. Our calculations show that La$_3$Ni$_2$O$_7$ is a multi-orbital Hund metal. Both the 3$d_{z^2}$ and 3$d_{x^2 - y^2}$ orbitals of Ni are close to be half filled and contribute the bands across the Fermi level. Band renormalization and orbital selective electronic correlation are observed. Through imaginary-time correlation functions, the discovery of high-spin configuration, spin-frozen phase, and spin-orbital separation shows that the system is in a frozen moment phase at high temperatures above 290 K and is a Fermi liquid at low temperatures, which is further comfirmed by the calculated spin, orbital, and charge susceptibilities under high temperatures. Our study uncovers Hundness in La$_3$Ni$_2$O$_7$ under high pressure., Comment: 6 pages, 5 figures, 2 tables

Possible high-$T_c$ superconductivity (SC) has been found experimentally in the bilayer material La$_3$Ni$_2$O$_7$ under high pressure recently, in which the Ni-$3d_{3z^2-r^2}$ and $3d_{x^2-y^2}$ orbitals are expected to play a key role in the electronic structure and the SC. Here we study the two-orbital electron correlations and the nature of the SC using the bilayer two-orbital Hubbard model downfolded from the band structure of La3Ni2O7 in the framework of the dynamical mean-field theory. We find that each of the two orbitals forms $s_\pm$-wave SC pairing. Because of the nonlocal inter-orbital hoppings, the two-orbital SCs are concomitant and they transition to Mott insulating states simultaneously when tuning the system to half filling. The Hund's coupling induced local inter-orbital spin coupling enhances the electron correlations pronouncedly and is crucial to the SC., Comment: 5 pages, 5 figures

Neural-network quantum states (NQSs), variationally optimized by combining traditional methods and deep learning techniques, is a new way to find quantum many-body ground states and gradually becomes a competitor of traditional variational methods. However, there are still some difficulties in the optimization of NQSs, such as local minima, slow convergence, and sign structure optimization. Here, we split a quantum many-body variational wave function into a multiplication of a real-valued amplitude neural network and a sign structure, and focus on the optimization of the amplitude network while keeping the sign structure fixed. The amplitude network is a convolutional neural network (CNN) with residual blocks, namely a ResNet. Our method is tested on three typical quantum many-body systems. The obtained ground state energies are lower than or comparable to those from traditional variational Monte Carlo (VMC) methods and density matrix renormalization group (DMRG). Surprisingly, for the frustrated Heisenberg $J_1$-$J_2$ model, our results are better than those of the complex-valued CNN in the literature, implying that the sign structure of the complex-valued NQS is difficult to be optimized. We will study the optimization of the sign structure of NQSs in the future., Comment: 9 pages, 3 figures, 4 tables, published version

Using the natural orbitals renormalization group, we studied the problem of a localized spin- 1/2 impurity coupled to two helical liquids via the Kondo interaction in a quantum spin Hall insulator, based on the Kane-Mele model defined in a finite zigzag graphene nanoribbon. We investigated the influence of the Kondo couplings with the helical liquids on both the static and dynamic properties of the ground state. The number and distinct spatial structures of the active natural orbitals (ANOs), which play essential roles in constructing the ground-state wave function, were first analyzed. Our numerical results indicate that two ANOs emerge, equal to the number of helical liquids. Specifically, at the coupling symmetry point, both ANOs are fully active with their spatial structures being respectively constituted by the different helical liquids. In comparison, when deviating from the symmetry point, only one ANO remains fully active, which is dominantly constructed by the helical liquid with the larger Kondo coupling. Local screening of the impurity, described by the impurity spin polarization and susceptibility, was further studied. It shows that at the coupling symmetry point, the impurity is maximally polarized and the spin susceptibility reaches the maximum. On the contrary, the impurity tends to be screened without polarization when the Kondo couplings deviate well from the symmetry point. The Kondo screening cloud, manifested by the spin correlation between the impurity and the conduction electrons, was finally explored. It is demonstrated that the Kondo cloud is mainly formed by the helical liquid with the larger Kondo coupling to the impurity. On the other hand, the spin-orbital coupling breaks the symmetry in spatial distribution of the spin correlation, leading to anisotropy in the Kondo cloud.

The system takes the embedded system single chip as the core, and organizes the gamma ray induction module, keying switch, radiation source braid locking mechanism, on-site alarm equipment, NB-IoT communication module, GPS positioning system and other related equipment to realize real-time operators warning and remote alarming to the monitoring platform. Thus, timely management of the fallen radiation source can be realized. What's more, position of radiation source braid is monitored by radiation source braid locking mechanism, gamma-ray induction module and keying switch. This idea solves the bottleneck problem of difficulty in transmitting real-time remote monitoring signal by NB-IoT technology. Single-chip microcontroller is innovatively embedded around the radiation source to monitor whether the source falls off. As a result, when part or all of the source braid are not returned to the storage location of the flaw detector, the gamma ray induction module, keying switch, and radiation source braid locking mechanism remind the operator of the dangerous situation. At the same time, NB-IoT transmission system alarms companies and regulators of safety risks. In conclusion, radiation source can be found timely when it falls off, to avoid radiation accident. Two bottlenecks related to installing GPS positioning system can be solved, that the GPS positioning system is installed on the flaw detector, and the radioactive source is still unable to be controlled, and that the GPS positioning system requires power to transmit the signal back to the regulatory platform or to the platform of the flaw detection enterprise. This article's ideas can solve this problem through the advantages of NB-IoT.

The natural orbitals renormalization group (NORG) has previously been proposed as an efficient numerical method for solving zero-temperature properties of multisite and multiorbital quantum impurity systems. Here, we implement the NORG as an impurity solver for dynamical mean-field theory (DMFT). In comparison with the exact diagonalization method, the NORG method can treat much more bath sites in an impurity model to which the DMFT maps a lattice model and can find accurate zero-temperature Matsubara and low-frequency retarded Green's functions. We demonstrate the effectiveness of this method on a two-orbital Hubbard model on the Bethe lattice and find successfully the orbital selective Mott transition with a Kondo resonance peak in the wide band and two holon-doublon bound state excitation peaks in the narrow band., Comment: 7 pages, 6 figures

With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system. In particular, the abnormal signals may emulate the normal signals, which makes it very challenging for abnormal signal recognition. In this paper, we propose a new abnormal signal recognition scheme, which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals. Firstly, we emulate synthetic abnormal communication signals including seven jamming patterns. Then, we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver. To improve the performance, we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm. Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations, even under low signal-to-noise ratio (SNR) and low jamming-to-signal ratio (JSR) conditions., Comment: Accepted by China Communications on August 30, 2021

A main task in condensed-matter physics is to recognize, classify, and characterize phases of matter and the corresponding phase transitions, for which machine learning provides a new class of research tools due to the remarkable development in computing power and algorithms. Despite much exploration in this new field, usually different methods and techniques are needed for different scenarios. Here, we present SimCLP: a simple framework for contrastive learning phases of matter, which is inspired by the recent development in contrastive learning of visual representations. We demonstrate the success of this framework on several representative systems, including classical and quantum, single-particle and many-body, conventional and topological. SimCLP is flexible and free of usual burdens such as manual feature engineering and prior knowledge. The only prerequisite is to prepare enough state configurations. Furthermore, it can generate representation vectors and labels and hence help tackle other problems. SimCLP therefore paves an alternative way to the development of a generic tool for identifying unexplored phase transitions., Comment: 7 pages, 6 figures

Two spins located at the edge of a quantum spin Hall insulator may interact with each other via indirect spin-exchange interaction mediated by the helical edge states, namely the RKKY interaction, which can be measured by the magnetic correlation between the two spins. By means of the newly developed natural orbitals renormalization group (NORG) method, we investigated the magnetic correlation between two Kondo impurities interacting with the helical edge states, based on the Kane-Mele model defined in a finite zigzag graphene nanoribbon with spin-orbital coupling (SOC). We find that the SOC effect breaks the symmetry in spatial distribution of the magnetic correlation, leading to anisotropy in the RKKY interaction. Specifically, the total correlation is always ferromagnetic (FM) when the two impurities are located at the same sublattice, while it is always antiferromagnetic (AFM) when at the different sublattices. Meanwhile, the behavior of the in-plane correlation is consistent with that of the total correlation. However, the out-of-plane correlation can be tuned from FM to AFM by manipulating either the Kondo coupling or the interimpurity distance. Furthermore, the magnetic correlation is tunable by the SOC, especially that the out-of-plane correlation can be adjusted from FM to AFM by increasing the strength of SOC. Dynamic properties of the system, represented by the spin-staggered excitation spectrum and the spin-staggered susceptibility at the two impurity sites, are finally explored. It is shown that the spin-staggered susceptibility is larger when the two impurities are located at the different sublattices than at the same sublattice, which is consistent with the behavior of the out-of-plane correlation. On the other hand, our study further demonstrates that the NORG is an effective numerical method for studying the quantum impurity systems., Comment: 9 pages, 9 figures

Using the natural orbitals renormalization group (NORG) method, we have investigated the screening of the local spin of an Anderson impurity interacting with the helical edge states in a quantum spin Hall insulator. We find that there is a local spin formed at the impurity site and the local spin is completely screened by electrons in the quantum spin Hall insulator. Meanwhile, the local spin is screened dominantly by a single active natural orbital. We then show that the Kondo screening mechanism becomes transparent and simple in the framework of natural orbitals formalism. We project the active natural orbital respectively into real space and momentum space to characterize its structure. And we confirm the spin-momentum locking property of the edge states based on the occupancy of a Bloch state in the edge to which the impurity couples. Furthermore, we study the dynamical property of the active natural orbital represented by the local density of states, from which we observe the Kondo resonance peak., Comment: 5 pages, 6 figures

A multichannel Kondo model, where two or more equivalent but independent channels of electrons compete to screen a spin-1/2 impurity, shows overcompensation of the impurity spin, leading to the non-Fermi-liquid behavior in various thermodynamic and transport properties. However, when the channel symmetry is broken, an impurity quantum phase transition can occur at zero temperature. Identification of an order parameter describing the impurity quantum phase transition is very difficult since it is beyond the conventional Landau-Ginzburg-Wilson theory. By employing the natural orbitals renormalization group method, we study both two-channel and threechannel Kondo models, from the perspective of spin correlation between the impurity and electrons in electronic channels. Here we demonstrate that by introducing the spin-correlation ratio as an order parameter we can characterize impurity quantum phase transitions driven by channel asymmetry. In particular, the universal critical exponents $\beta$ of the spin-correlation ratio and $\nu$ of the correlation length are explicitly determined by finite-sizescaling analysis, namely, $\beta = 0.10(1), \nu = 2.0(1)$, and $\beta = 0.10(1), \nu = 2.5(1)$ for the two-channel and three-channel Kondo models, respectively., Comment: 7 pages, 7 figures