arXiv subjects

Ming Xue

Publications and source records attributed to Ming Xue.

At least 19 recordsLinked to original sources

arXiv2026

Defect engineering of ultrathin gallium nitride via electric fields for advanced electronic, magnetic, and gas sensing applications

Scaling wide-band-gap semiconductors to the ultrathin limit offers a transformative pathway for power electronics, with gallium nitride (GaN) representing a cornerstone material in this class. However, the operational resilience and functional tunability of its two-dimensional form (g-GaN) remain underexplored. This work shifts the focus from idealized systems to the complex materials behavior under realistic conditions, investigating how the synergistic effects of point vacancy defects, strain, and external electric fields govern its electronic, magnetic, and sensing landscapes. We demonstrate that these factors are not merely perturbations but are fundamental to modulating the material response. Our first-principles calculations suggest g-GaN maintains electronic stability under intense electric fields; notably, gallium vacancies are predicted to further extend the theoretical stability limit. While in-plane tension preserves the band gap evolution under an electric field, in-plane compression facilitates low-field metallization. Using nitrogen monoxide (NO) adsorption as a prototype, we find that the interaction is defect-modulated and potentially tunable by electric fields. Analysis of adsorption energetics and diffusion barriers suggests the gallium vacancy may act as a thermodynamic trap for NO. Targeted hybrid-functional (HSE06) validation confirms the reliability of observed adsorption trends and theoretical metallization thresholds, while revealing that precise electronic-exchange treatment is critical for capturing the magnetic ground state of nitrogen vacancies. By systematically examining the geometry, energetics, band structure, density of states, magnetic response, and charge transfer, this study clarifies the interplay between defects and external electric fields, providing insights into theoretical upper bounds for property tuning and semiconductor device engineering.

cond-mat.mtrl-sci↗

arXiv2026

Scalable high-fidelity and near-deterministic preparation of large photon-number states

The scalable preparation of large photon-number (Fock) states is a long-standing frontier in quantum science, with direct implications for quantum metrology and bosonic quantum information processing. Despite substantial progress at small photon numbers, extending state generation to large photon numbers while maintaining high fidelity and operating deterministically remains a significant challenge. Here we demonstrate a scalable and experimentally accessible control protocol for generating large photon-number states using only native spin--oscillator operations. The protocol alternates Jaynes--Cummings interactions with phase-space displacements to imprint photon-number--dependent phases and convert them into selective interference in photon-number space. It already achieves high preparation fidelity unconditionally, while an optional final qubit projection removes residual qubit--field correlations and further enhances the fidelity. Conditioned on this final projection, photon-number state preparation with fidelities exceeding $0.95$ is achieved for photon numbers in the few-hundred regime, with a success probability exceeding $0.90$, placing the protocol in a near-deterministic operating regime. The resulting control sequences remain shallow and are robust against detuning, control noise, and experimentally relevant dissipation. Our results establish a practical route to scalable, high-fidelity photon-number state preparation at large photon numbers and provide a versatile interference-engineering toolbox for nonclassical bosonic state synthesis.

quant-ph↗

arXiv2025

AIMNET: An IoT-Empowered Digital Twin for Continuous Gas Emission Monitoring and Early Hazard Detection

A Digital Twin (DT) framework to enhance carbon-based gas plume monitoring is critical for supporting timely and effective mitigation responses to environmental hazards such as industrial gas leaks, or wildfire outbreaks carrying large carbon emissions. We present AIMNET, a one-of-a-kind DT framework that integrates a built-in-house Internet of Things (IoT)-based continuous sensing network with a physics-based multi-scale weather-gas transport model, that enables high-resolution and real-time simulation and detection of carbon gas emissions. AIMNET features a three-layer system architecture: (i) physical world: custom-built devices for continuous monitoring; (ii) bidirectional information feedback links: intelligent data transmission and reverse control; and (iii) digital twin world: AI-driven analytics for prediction, anomaly detection, and dynamic weather-gas coupled molecule transport modeling. Designed for scalable, energy-efficient deployment in remote environments, AIMNET architecture is realized through a small-scale distributed sensing network over an oil and gas production basin. To demonstrate the high-resolution, fast-responding concept, an equivalent mobile-based emission monitoring network was deployed around a wastewater treatment plant that constantly emits methane plumes. Our preliminary results through which, have successfully captured the methane emission events whose dynamics have been further resolved by the tiered model simulations. This work supports our position that AIMNET provides a promising DT framework for reliable, real-time monitoring and predictive risk assessment. In the end, we also discuss key implementation challenges and outline future directions for advancing such a new DT framework for translation deployment.

cs.NI↗

arXiv2025

Improving adiabatic quantum factorization via chopped random-basis optimization

Integer factorization remains a significant challenge for classical computers and is fundamental to the security of RSA encryption. Adiabatic quantum algorithms present a promising solution, yet their practical implementation is limited by the short coherence times of current NISQ devices and quantum simulators. In this work, we apply the chopped random-basis (CRAB) optimization technique to enhance adiabatic quantum factorization algorithms. We demonstrate the effectiveness of CRAB by applying it to factor the integers ranging from 21 to 2479, achieving significantly improved fidelity of the target state when the evolution time exceeds the quantum speed limit. Notably, this performance improvement shows resilience in the presence of dephasing noise, highlighting CRAB's practical utility in noisy quantum systems. Our findings suggest that CRAB optimization can serve as a powerful tool for advancing adiabatic quantum algorithms, with broader implications for quantum information processing tasks.

quant-ph↗

arXiv2025

Synergizing RAG and Reasoning: A Systematic Review

Recent breakthroughs in large language models (LLMs), particularly in reasoning capabilities, have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels. By synergizing retrieval mechanisms with advanced reasoning, LLMs can now tackle increasingly complex problems. This paper presents a systematic review of the collaborative interplay between RAG and reasoning, clearly defining "reasoning" within the RAG context. It construct a comprehensive taxonomy encompassing multi-dimensional collaborative objectives, representative paradigms, and technical implementations, and analyze the bidirectional synergy methods. Additionally, we critically evaluate current limitations in RAG assessment, including the absence of intermediate supervision for multi-step reasoning and practical challenges related to cost-risk trade-offs. To bridge theory and practice, we provide practical guidelines tailored to diverse real-world applications. Finally, we identify promising research directions, such as graph-based knowledge integration, hybrid model collaboration, and RL-driven optimization. Overall, this work presents a theoretical framework and practical foundation to advance RAG systems in academia and industry, fostering the next generation of RAG solutions.

cs.IR↗

arXiv2024

Enhancing Dynamic Range of Sub-Quantum-Limit Measurements via Quantum Deamplification

Balancing high sensitivity with a broad dynamic range is a fundamental challenge in measurement science, as improving one often compromises the other. While traditional quantum metrology has prioritized enhancing local sensitivity, a large dynamic range is crucial for applications such as atomic clocks, where extended phase interrogation times contribute to wider phase range. In this Letter, we introduce a novel quantum deamplification mechanism that extends dynamic range at a minimal cost of sensitivity. Our approach uses two sequential spin-squeezing operations to generate and detect an entangled probe state, respectively. We demonstrate that the optimal quantum interferometer limit can be approached through two-axis counter-twisting dynamics. Further expansion of dynamic range is possible by using sequential quantum deamplification interspersed with phase encoding processes. Additionally, we show that robustness against detection noise can be enhanced by a hybrid sensing scheme that combines quantum deamplification with quantum amplification. Our protocol is within the reach of state-of-the-art atomic-molecular-optical platforms, offering a scalable, noise-resilient pathway for entanglement-enhanced metrology.

quant-ph↗

arXiv2024

Efficient generation of multiqubit entanglement states using rapid adiabatic passage

We propose the implementation of a rapid adiabatic passage (RAP) scheme to generate entanglement in Rydberg atom-array systems. This method transforms a product state in a multi-qubit system into an entangled state with high fidelity and robustness. By employing global and continuous driving laser fields, we demonstrate the generation of two-qubit Bell state and three-qubit W state, via sequential RAP pulses within the Rydberg blockade regime. As an illustrative example, applying this technique to alkali atoms, we predict fidelities exceeding 0.9995 for two-qubit Bell and three-qubit W state, along with excellent robustness. Furthermore, our scheme can be extended to generate entanglement between weakly coupled atoms and to create four-qubit Greenberger- Horne-Zeilinger states through spatial correlations. Our approach holds the potential for extension to larger atomic arrays, offering a straightforward and efficient method to generate high-fidelity entangled states in neutral atom systems.

quant-ph↗

arXiv2024

High-fidelity and robust controlled-Z gates implemented with Rydberg atoms via echoing rapid adiabatic passage

High-fidelity and robust quantum gates are essential for quantum information processing, where neutral Rydberg atoms trapped in optical tweezer arrays serving as a versatile platform for the implementation. We propose a rapid adiabatic passage (RAP) scheme for achieving a high-fidelity controlled-Z (CZ) gate on a neutral atom Rydberg platform. Utilizing only global laser dressing, our scheme involves echoing two identical RAP pulses within the Rydberg blockade regime to realize a CZ gate and can be readily extended to a C$^k$Z gate with additional qubits. We predict a CZ gate with fidelity over 0.9995 using akali-atom parameters, and a CCZ gate with fidelity exceeding 0.999. Moreover, the direct utilization of echoing RAP pulses enables the implementation of a four-bit CCCZ gate at fidelity over 0.996 without further optimization. The proposed scheme, remarkably robust to variations in driving fields and realistic decoherence effects, holds promise for future quantum information processing applications.

quant-ph↗

arXiv2024

Miscibility of Binary Bose-Einstein Condensates with $p$-wave Interaction

We investigate the ground-state phase diagram of a binary mixture of Bose-Einstein condensates (BECs) with competing interspecies $s$- and $p$-wave interactions. Exploiting a pseudopotential model for the $l=1$ partial wave, we derive an extended Gross-Pitaevskii (GP) equation for the BEC mixture that incorporates both $s$- and $p$-wave interactions. Based on it, we study the miscible-immiscible transition of a binary BEC mixture in the presence of interspecies $p$-wave interaction, by combining numerical solution of the GP equation and Gaussian variational analysis. Our study uncovers a dual effect -- either enhance or reduce miscibility -- of positive interspecies $p$-wave interaction, which can be precisely controlled by adjusting relevant experimental parameters. By complete characterizing the miscibility phase diagram, we establish a promising avenue towards experimental control of the miscibility of binary BEC mixtures via high partial-wave interactions.

cond-mat.quant-gas↗

arXiv2023

Fermi level depinning via insertion of a graphene buffer layer at the gold-2D tin monoxide contact

Two-dimensional (2D) tin monoxide (SnO) has attracted much attention owing to its distinctive electronic and optical properties, which render itself suitable as a channel material in field effect transistors (FETs). However, upon contact with metals for such applications, the Fermi level pinning effect may occur, where states are induced in its band gap by the metal, hindering its intrinsic semiconducting properties. We propose the insertion of graphene at the contact interface to alleviate the metal-induced gap states. By using gold (Au) as the electrode material and monolayer SnO (mSnO) as the channel material, the geometry, bonding strength, charge transfer and tunnel barriers of charges, and electronic properties including the work function, band structure, density of states, and Schottky barriers are thoroughly investigated using first-principles calculations for the structures with and without graphene to reveal the contact behaviours and Fermi level depinning mechanism. It has been demonstrated that strong covalent bonding is formed between gold and mSnO, while the graphene interlayer forms weak van der Waals interaction with both materials, which minimises the perturbance to the band structure of mSnO. The effects of out-of-plane compression are also analysed to assess the performance of the contact under mechanical deformation, and a feasible fabrication route for the heterostructure with graphene is proposed. This work systematically explores the properties of the Au-mSnO contact for applications in FETs and provides thorough guidance for future exploitation of 2D materials in various electronic applications and for selection of buffer layers to improve metal-semiconductor contact.

cond-mat.mtrl-sci↗

arXiv2023

Machine learning for predicting fatigue properties of additively manufactured materials

Fatigue properties of additively manufactured (AM) materials depend on many factors such as AM processing parameter, microstructure, residual stress, surface roughness, porosities, post-treatments, etc. Their evaluation inevitably requires these factors combined as many as possible, thus resulting in low efficiency and high cost. In recent years, their assessment by leveraging the power of machine learning (ML) has gained increasing attentions. Here, we present a comprehensive overview on the state-of-the-art progress of applying ML strategies to predict fatigue properties of AM materials, as well as their dependence on AM processing and post-processing parameters such as laser power, scanning speed, layer height, hatch distance, built direction, post-heat temperature, etc. A few attempts in employing feedforward neural network (FNN), convolutional neural network (CNN), adaptive network-based fuzzy system (ANFS), support vector machine (SVM) and random forest (RF) to predict fatigue life and RF to predict fatigue crack growth rate are summarized. The ML models for predicting AM materials' fatigue properties are found intrinsically similar to the commonly used ones, but are modified to involve AM features. Finally, an outlook for challenges (i.e., small dataset, multifarious features, overfitting, low interpretability, unable extension from AM material data to structure life) and potential solutions for the ML prediction of AM materials' fatigue properties is provided.

cond-mat.mtrl-sci↗

arXiv2023

Cyclic nonlinear interferometry with entangled non-Gaussian spin states

We propose an efficient nonlinear readout scheme for entangled non-Gaussian spin states (ENGSs) based on the intrinsic quasi-cyclic dynamics of interacting spin-1/2 systems. We focus on two well-known spin models of twist-and-turn (TNT) and two-axis-counter-twisting (TACT), where ENGS can be generated by spin dynamics starting from unstable fixed points. In the TNT model, non-Gaussian probe state evolves directly back to the vicinity of initial state during the subsequent time-forward evolution for path recombining, accompanied by quantum magnification of encoded signal and refocusing of the associated quantum noise. Based on low-order moment measurement, we find the optimal metrological gain nearly saturates the quantum Cramer-Rao bound (QCRB) and follows the Heisenberg scaling. For the TACT case, the QCRB can also be nearly approached when the state converges to either of the two unstable fixed points, respectively corresponding to the initial state or its orthogonal coherent state. The latter case goes beyond previous studies where tracing back to or crossing the initial states is mostly considered. The present protocol does not require time-reversal as in typical nonlinear interferometries, and it also avoids complicated measurement of nonlinear observables or full probability distributions. The operational approach we discuss presents a practical way for realizing high-precision and detection-noise-robust quantum metrology with ENGS.

quant-ph↗

arXiv2022

Modeling and simulation of sintering process across scales

Sintering, as a thermal process at elevated temperature below the melting point, is widely used to bond contacting particles into engineering products such as ceramics, metals, polymers, and cemented carbides. Modelling and simulation as important complement to experiments are essential for understanding the sintering mechanisms and for the optimization and design of sintering process. We share in this article a state-to-the-art review on the major methods and models for the simulation of sintering process at various length scales. It starts with molecular dynamics simulations deciphering atomistic diffusion process, and then moves to microstructure-level approaches such as discrete element method, Monte--Carlo method, and phase-field models, which can reveal subtle mechanisms like grain coalescence, grain rotation, densification, grain coarsening, etc. Phenomenological/empirical models on the macroscopic scales for estimating densification, porosity and average grain size are also summarized. The features, merits, drawbacks, and applicability of these models and simulation technologies are expounded. In particular, the latest progress on the modelling and simulation of selective and direct-metal laser sintering based additive manufacturing is also reviewed. Finally, a summary and concluding remarks on the challenges and opportunities are given for the modelling and simulations of sintering process.

cond-mat.mtrl-sci↗

arXiv2022

Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlo

Neural network quantum states (NQS), incorporating with variational Monte Carlo (VMC) method, are shown to be a promising way to investigate quantum many-body physics. Whereas vanilla VMC methods perform one gradient update per sample, we introduce a novel objective function with proximal optimization (PO) that enables multiple updates via reusing the mismatched samples. Our VMC-PO method keeps the advantage of the previous importance sampling gradient optimization algorithm [L. Yang, {\it et al}, Phys. Rev. Research {\bf 2}, 012039(R)(2020)] that efficiently uses sampled states. PO mitigates the numerical instabilities during network updates, which is similar to stochastic reconfiguration (SR) methods, but achieves an alternative and simpler implement with lower computational complexity. We investigate the performance of our VMC-PO algorithm for ground-state searching with a 1-dimensional transverse-field Ising model and 2-dimensional Heisenberg antiferromagnet on a square lattice, and demonstrate that the reached ground-state energies are comparable to state-of-the-art results.

cond-mat.dis-nn↗

arXiv2022

Characterizing quantum criticality and steered coherence in the XY-Gamma chain

In this paper, we show that an effective spin Hamiltonian with various types of couplings can be engineered using quantum simulators in atomic-molecular-optical laboratories, dubbed the \emph{XY}-Gamma model. We analytically solve the one-dimensional short-range interacting case with the Jordan-Wigner transformation and establish the phase diagram. In the gapless phase, an incommensurate spiral order is manifested by the vector-chiral correlations. Between distinct gapped phases, a logarithmic scaling behavior of local measures, including spin correlations and the steered quantum coherence, is identified for the quantum critical points, yielding a compelling value of the correlation-length critical exponent. We derive explicit scaling forms of the excitation gap near the quantum critical points. The extracted critical exponents reveal the quantum phase transition on the boundary of Tomonaga-Luttinger liquid belongs to Lifshitz universality class.Our results may provide useful insights into the underlying mechanism in quantum criticality for state-of-the-art experiments of quantum simulation.

quant-ph↗

arXiv2022

Controlling atomic spin-mixing via multiphoton transitions in a cavity

We propose to control spin-mixing dynamics in a gas of spinor atoms, via the combination of two off-resonant Raman transition pathways, enabled by a common cavity mode and a bichromatic pump laser. The mixing rate, which is proportional to the synthesized spin-exchange interaction strength, and the effective atomic quadratic Zeeman shift (QZS), can both be tuned by changing the pump laser parameters. Quench and driving dynamics of the atomic collective spin are shown to be controllable on a faster time scale than in existing experiments based on inherent spin-exchange collision interactions. The results we present open a promising avenue for exploring spin-mixing physics of atomic ensembles accessible in current experiments.

cond-mat.quant-gas↗

arXiv2022

Highly stable electronic properties of rippled antimonene under compressive deformation

Antimonene has attracted much attention for its high carrier mobility and suitable band gap for electronic, optoelectronic, and even spintronic devices. To tailor its properties for such applications, strain engineering may be adopted. However, such 2D crystals may prefer to exist in the rippled form due to the instability of long-range orders, and rippling has been shown to have a contrasting, significant impact on the electronic properties of various 2D materials, which complicates the tuning process. Hence, the effects of rippling on the electronic properties of antimonene under strain are herein investigated by comparing antimonene in its rippled and flat forms. DFT calculations are performed to compute the structural and electronic parameters, where uniaxial compression of up to 7.5% is applied along the armchair and zigzag directions to study the anisotropic behavior of the material. Highly stable properties such as the work function and band gap are obtained for the rippled structures, where they are fully relaxed, regardless of the compression level, and these properties do not deviate much from those of the pristine structure under no strain. In contrast, various changes are observed in their flat counterparts. The mechanisms behind the different results are thoroughly explained by analyses of the density of states and structure geometry. The out-of-plane dipole moments of the rippled structures are also presented to give further insights into potential applications of rippled antimonene in sensors, actuators, triboelectric nanogenerators, etc. This work presents extensive data and thorough analysis on the effect of rippling on antimonene. The identification of optimal ripple amplitudes for which the electronic properties of the pristine condition can be recovered will be highly significant in guiding the rational design and architecture of antimonene-based devices.

cond-mat.mtrl-sci↗

arXiv2021

Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning

An energy gap develops near quantum critical point of quantum phase transition in a finite many-body (MB) system, facilitating the ground state transformation by adiabatic parameter change. In real application scenarios, however, the efficacy for such a protocol is compromised by the need to balance finite system life time with adiabaticity, as exemplified in a recent experiment that prepares three-mode balanced Dicke state near deterministically [PNAS {\bf 115}, 6381 (2018)]. Instead of tracking the instantaneous ground state as unanimously required for most adiabatic crossing, this work reports a faster sweeping policy taking advantage of excited level dynamics. It is obtained based on deep reinforcement learning (DRL) from a multi-step training scheme we develop. In the absence of loss, a fidelity $\ge 99\%$ between prepared and the target Dicke state is achieved over a small fraction of the adiabatically required time. When loss is included, training is carried out according to an operational benchmark, the interferometric sensitivity of the prepared state instead of fidelity, leading to better sensitivity in about half of the previously reported time. Implemented in a Bose-Einstein condensate of $\sim 10^4$ $^{87}$Rb atoms, the balanced three-mode Dicke state exhibiting an improved number squeezing of $13.02\pm0.20$ dB is observed within 766 ms, highlighting the potential of DRL for quantum dynamics control and quantum state preparation in interacting MB systems.

cond-mat.quant-gas↗