arXiv subjects

Nhat Minh Nguyen

Publications and source records attributed to Nhat Minh Nguyen.

7 recordsLinked to original sources

arXiv2026

Facile hBN-hBN Interfacial Overlap Engineering for Enhanced Quantum Emitter Formation

Quantum emitters in two-dimensional materials, particularly hBN, are promising platforms for quantum technologies. However, achieving high-density emitters at predetermined locations while preserving optical quality remains challenging. Here, we introduce a facile, cost-effective double-layer all-dry transfer approach to deterministically create overlap regions between hBN flakes. These pre-defined capped regions exhibit a significantly enhanced emitter density, with up to a 15-fold increase compared to uncapped areas. Importantly, this method does not compromise emitter quality: emitters within overlap regions demonstrate excellent optical performance, including high signal-to-background and signal-to-noise ratios, large Debye-Waller factors, high brightness, and strong spectral stability. Possible defect configurations are also discussed to contextualize the observed emission characteristics. This scalable strategy enables preferential formation of quantum emitters in targeted regions, achieving higher densities than simple treatments such as plasma irradiation while avoiding the complexity of advanced fabrication techniques. The approach provides a practical pathway for integrating high-quality quantum emitters into scalable quantum photonic platforms.

cond-mat.mtrl-sci↗

arXiv2026

Physics-Based versus Data-Driven Classification of Single-Photon Quantum Emitters from Sparse Autocorrelation Data

Identifying single photon emitters from large, inhomogeneous candidate populations is key to realizing many quantum applications. This requires measuring the emitters second order autocorrelation function, whose statistical reliability is fundamentally limited by acquisition time. Machine learning classifiers can accelerate identification from sparse data, but their performance relative to physics-based inference has not been systematically examined. Here, we introduce sequential Bayesian inference for single photon emitter classification and benchmark it against Levenberg-Marquardt fitting and a feedforward neural network. We use synthetic training and test data calibrated against real Hanbury Brown-Twiss measurements from hexagonal boron nitride emitters, enabling evaluation against an exactly known ground-truth emitter number under realistic noise and background conditions. All three approaches achieve high, near-perfect accuracy with sufficient integration time, but differ greatly in convergence rate and robustness under sparse photon statistics. The neural network is most robust at short integration times. The Bayesian classifier reaches near-perfect accuracy fastest, while retaining full physical interpretability. Levenberg-Marquardt fitting remains a valuable, fully interpretable method, achieving the highest recall despite being the slowest to converge. These results lead to several key conclusions. No single method dominates across all performance metrics. Relying on any one metric alone can give a misleading picture of classifier performance, particularly under sparse photon statistics. Physics-based and data-driven methods are complementary rather than competing approaches. Together, these findings provide practical guidance for selecting and combining classification strategies for scalable single-photon-source screening and other quantum-emitter characterization tasks.

quant-ph↗

arXiv2026

Nanoscale Fluorescence Thermometry: Probes, Recent Advances and Emerging Directions

The transition of materials and devices to nanometer, atomic, and quantum scales makes thermal characterization increasingly challenging, driving the need for advanced nanoscale thermometry. Fluorescence nanothermometry has emerged as a powerful approach, enabling remote, spatially resolved temperature measurements with sub-micrometer-to-nanometer precision across applications in nanoelectronics, microfluidics, and biological systems. In these systems, temperature is inferred from variations in fluorescence observables, including spectral position, intensity, linewidth, and excited-state dynamics. This review provides a comprehensive and critical overview of fluorescence nanothermometry, covering fundamental mechanisms, material platforms, recent advances, and emerging applications. It further presents a critical evaluation of key challenges and discusses emerging strategies and future research directions toward achieving robust, real-time thermometry. It is anticipated that this review will stimulate further advances in material platforms and system design, accelerating the development of accurate, scalable, and application-ready nanoscale thermometers.

physics.optics↗

arXiv2025

Laser-Induced Heating in Diamonds: Influence of Substrate Thermal Conductivity and Interfacial Polymer Layers

Diamonds hosting color centers possess intrinsically high thermal conductivity; therefore, laser-induced heating has often received little attention. However, when placed on substrates with low thermal conductivity, localized heating of diamonds under laser excitation can become significant, and the presence of an interfacial polymer layer between substrate and diamond further amplifies this effect. Yet, the relationship between substrate thermal conductivity, polymer thickness, and laser heating remains to be established. Here, a systematic investigation is presented on laser-induced heating of silicon-vacancy diamond on substrates with varying thermal conductivity and interfacial polymer thickness. Results reveal that even at a low excitation power of 737~$μ$W/$μ$m$^2$, thin amorphous holey carbon -- the lowest-conductivity substrate ($\sim$0.2~W~m$^{-1}$~K$^{-1}$) studied -- exhibits substantial heating, while glass ($\sim$1.4~W~m$^{-1}$~K$^{-1}$) and polydimethylsiloxane (PDMS, $\sim$0.35~W~m$^{-1}$~K$^{-1}$) show noticeable heating only above 2.95~mW/$μ$m$^2$. For polymer interlayers, a thickness of just 2.2~$μ$m induces significant heating at 2.95~mW/$μ$m$^2$ and above, highlighting strong influence of both substrate and polymer thickness on local heating response. Experimental findings are further validated using COMSOL Multiphysics simulations with a steady-state 3D heat transfer model. These results provide practical guidance for substrate selection and sample preparation, enabling optimization of conditions for optical thermometry and quantum sensing applications.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Emitters in Hexagonal Boron Nitride: Principles, Engineering and Applications

Solid-state quantum emitters, molecular-sized complexes releasing a single photon at a time, have garnered much attention owing to their use as a key building block in various quantum technologies. Among these, quantum emitters in hexagonal boron nitride (hBN) have emerged as front runners with superior attributes compared to other competing platforms. These attributes are attainable thanks to the robust, two-dimensional lattice of the material formed by the extremely strong B-N bonds. This review discusses the fundamental properties of quantum emitters in hBN and highlights recent progress in the field. The focus is on the fabrication and engineering of these quantum emitters facilitated by state-of-the-art equipment. Strategies to integrate the quantum emitters with dielectric and plasmonic cavities to enhance their optical properties are summarized. The latest developments in new classes of spin-active defects, their predicted structural configurations, and the proposed suitable quantum applications are examined. Despite the current challenges, quantum emitters in hBN have steadily become a promising platform for applications in quantum information science.

cond-mat.mtrl-sci↗

arXiv2024

Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control

Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehicles remain critical challenges, particularly in the dynamic aquatic environment. In this work, we propose a novel control scheme for creating multi-agent distributed formation control with limited communication between individual agents. In addition, the formation of the multi-agent can be reconfigured in real-time and the network connectivity can be maintained. The proposed use case for this scheme includes creating underwater mobile communication networks that can adapt to environmental or network conditions to maintain the quality of communication links for long-range exploration, seabed monitoring, or underwater infrastructure inspection. This work introduces a novel Distributed Nonlinear Model Predictive Control (DNMPC) strategy, integrating Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) with a relaxed decay rate, specifically tailored for 6-DOF underwater robotics. The effectiveness of our proposed DNMPC scheme was demonstrated through rigorous MATLAB simulations for trajectory tracking and formation reconfiguration in a dynamic environment. Our findings, supported by tests conducted using Software In The Loop (SITL) simulation, confirm the approach's applicability in real-time scenarios.

eess.SY↗

arXiv2019

A rigorous EFT-based forward model for large-scale structure

Conventional approaches to cosmology inference from galaxy redshift surveys are based on n-point functions, which are under rigorous perturbative control on sufficiently large scales. Here, we present an alternative approach, which employs a likelihood at the level of the galaxy density field. By integrating out small-scale modes based on effective-field theory arguments, we prove that this likelihood is under perturbative control if certain specific conditions are met. We further show that the information captured by this likelihood is equivalent to the combination of the next-to-leading order galaxy power spectrum, leading-order bispectrum, and BAO reconstruction. Combined with MCMC sampling and MAP optimization techniques, our results allow for fully Bayesian cosmology inference from large-scale structure that is under perturbative control. We illustrate this via a first demonstration of unbiased cosmology inference from nonlinear large-scale structure using this likelihood. In particular, we show unbiased estimates of the power spectrum normalization $σ_{8}$ from a catalog of simulated dark matter halos, where nonlinear information is crucial in breaking the $b_{1} - σ_{8}$ degeneracy.

astro-ph.CO↗