arXiv subjects

Hyeonsu Kim

Publications and source records attributed to Hyeonsu Kim.

8 recordsLinked to original sources

arXiv2026

Coherence-Based Identification of Carbon-Based Spin Qubits in Hexagonal Boron Nitride from First Principles

Carbon-related defects in hexagonal boron nitride are promising room-temperature single-spin qubits and quantum sensors, but their atomic structures remain largely unidentified. Here we show, using first-principles calculations of electron-spin decoherence, that the atomic structure of each defect is imprinted in its spin coherence. Mapping the Hahn-echo dynamics of seven candidate carbon defects across magnetic field and four isotope-engineered nuclear-spin baths, we find that electron-spin-echo envelope modulation emerges at defect-specific magnetic fields, at which the nearest-neighbor nuclear spins satisfy a cancellation condition set by their hyperfine and quadrupole couplings. Both the fields and the modulation frequencies follow from an analytical model using computed hyperfine and quadrupole tensors alone, and they shift or vanish upon isotope substitution. At low fields, the field dependence of the coherence time separates the defects into two classes according to the sublattice occupied by carbon. These decoherence fingerprints, directly testable in isotope-engineered samples, establish a structural identification route complementary to optical spectroscopy.

quant-ph↗

arXiv2026

A First-principles Computational Framework for Quantum Decoherence in Complex Diamond Spin Environments

Quantum decoherence induced by defects remains a major limitation for solid-state quantum technologies, yet predicting decoherence in realistic materials remains computationally challenging. Complex defect populations are often approximated as homogeneous spin baths, obscuring the role of defect-specific electronic structure and spin dynamics. Here, we develop a predictive framework for decoherence in diamond by combining first-principles electronic-structure calculations, quantum many-body spin-bath simulations, and experimental validation. The framework incorporates defect-resolved spin Hamiltonians and heterogeneous spin baths containing multiple paramagnetic defect species. Using diamond nitrogen-vacancy ensembles as a model platform, we investigate mixed nitrogen-, vacancy-, and hydrogen-related defect environments. We show that decoherence depends not only on defect density but also on defect identity and bath composition, whose distinct electronic structures, hyperfine interactions, and spin dynamics produce different coherence behavior. Heterogeneous defect populations can either suppress or enhance decoherence, producing trends unexplained by homogeneous-bath models. Magnetic-field-dependent Hahn-echo measurements on samples with different defect concentrations validate the framework. The calculations reproduce the observed coherence times and stretched-exponential decay behavior across a broad magnetic-field range and identify vacancy-related defects as critical contributors beyond the conventionally assumed P1 spin bath. By linking atomistic defect properties to quantum coherence, our framework provides a predictive route for identifying hidden defect environments and optimizing decoherence in defect-based quantum materials.

quant-ph↗

arXiv2026

Floquet analysis of coherence in periodically driven diamond NV ensemble systems

High-density nitrogen-vacancy (NV) ensembles are promising platforms for solid-state quantum sensing, but their performance is limited by dipolar interactions and inhomogeneous dephasing. Periodic decoupling sequences such as Waugh-Huber-Haeberlen (WAHUHA) can extend the observed stroboscopic decay time. However, it remains unclear that a longer effective dephasing time yield improved magnetic-field sensitivity. Here, we show that WAHUHA control increases the effective inhomogeneous dephasing time of a dense NV ensemble from $T_2^\ast$ of 0.9 ${\mu}$s to $T_{2,eff}^\ast$ of 31 ${\mu}$s, while producing little improvement in dc magnetic-field sensitivity. Using detuning-resolved stroboscopic spectroscopy and finite-pulse Floquet analysis, we show that the long-lived signal arises from phase wrapping and quasi-energy branch folding of the one-cycle unitary. These effects reshape the stroboscopic spectrum and suppress the detuning-to-phase transduction slope, $d\Phi/d\Delta$, which governs the dc magnetic-field response. Our results demonstrate that, under periodic driving, an extended effective dephasing time does not necessarily translate into enhanced dc sensitivity and establish finite-pulse Floquet analysis as a practical framework for evaluating coherence in spin ensembles.

quant-ph↗

arXiv2026

PIM-SHERPA: Software Method for On-device LLM Inference by Resolving PIM Memory Attribute and Layout Inconsistencies

On-device deployments of large language models (LLMs) are rapidly proliferating across mobile and edge platforms. LLM inference comprises a compute-intensive prefill phase and a memory bandwidth-intensive decode phase, and the decode phase has been widely recognized as well-suited to processing-in-memory (PIM) in both academia and industry. However, practical PIM-enabled systems face two obstacles between these phases, a memory attribute inconsistency in which prefill favors placing weights in a cacheable region for reuse whereas decode requires weights in a non-cacheable region to reliably trigger PIM, and a weight layout inconsistency between host-friendly and PIM-aware layouts. To address these problems, we introduce \textit{PIM-SHERPA}, a software-only method for efficient on-device LLM inference by resolving PIM memory attribute and layout inconsistencies. PIM-SHERPA provides two approaches, DRAM double buffering (DDB), which keeps a single PIM-aware weights in the non-cacheable region while prefetching the swizzled weights of the next layer into small cacheable buffers, and online weight rearrangement with swizzled memory copy (OWR), which performs the on-demand swizzled memory copy immediately before GEMM. Compared to a baseline PIM emulation system, PIM-SHERPA achieves approximately 47.8 - 49.7\% memory capacity savings while maintaining comparable performance to the theoretical maximum on the Llama 3.2 model. To the best of our knowledge, this is the first work to identify the memory attribute inconsistency and propose effective solutions on product-level PIM-enabled systems.

cs.DC↗

arXiv2025

Magnetic-field dependent VB- spin decoherence in hexagonal boron nitrides: A first-principles study

The negatively charged boron vacancy (VB-) in h-BN is a spin-1 defect functioning as an optically addressable spin qubit in two-dimensional materials. A precise understanding of its spin decoherence is essential to advance it into a robust qubit platform. First-principles quantum many-body simulations are employed to investigate VB- decoherence in dense nuclear spin baths of h-BN under magnetic fields from 0.01 to 3 T, considering isotopic variants h-10B14N, h-11B14N, h-10B15N, and h-11B15N. A transition boundary (TB) is observed where the dominant decoherence mechanism changes: below the TB, sub-microsecond decoherence is governed by independent nuclear spin dynamics, whereas above it, pairwise flip-flops dominate, extending T2 to tens of microseconds. Analytical predictions place the TB at 0.502 T for h-10B14N and 0.205 T for h-11B14N. The larger TB in h-10BN results from the larger nuclear spin of 10B (I = 3), which produces stronger nuclear modulation over a wider field range. The analytical approach also explains the magnetic-field-insensitive fast modulation observed below the TB. These findings clarify the role of dense nuclear spin baths with large nuclear spins (I >= 1) in VB- decoherence and provide design principles for isotopically engineered h-BN spin qubits.

quant-ph↗

arXiv2024

Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation

Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting. However, the lack of 3D awareness in the 2D diffusion models destabilizes score distillation-based methods from reconstructing a plausible 3D scene. To address this issue, we propose 3DFuse, a novel framework that incorporates 3D awareness into pretrained 2D diffusion models, enhancing the robustness and 3D consistency of score distillation-based methods. We realize this by first constructing a coarse 3D structure of a given text prompt and then utilizing projected, view-specific depth map as a condition for the diffusion model. Additionally, we introduce a training strategy that enables the 2D diffusion model learns to handle the errors and sparsity within the coarse 3D structure for robust generation, as well as a method for ensuring semantic consistency throughout all viewpoints of the scene. Our framework surpasses the limitations of prior arts, and has significant implications for 3D consistent generation of 2D diffusion models.

cs.CV↗

arXiv2024

Diffusion Model for Dense Matching

The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining hand-designed prior terms, which are difficult to formulate, recent approaches have focused on learning the data term with deep neural networks without explicitly modeling the prior, assuming that the model itself has the capacity to learn an optimal prior from a large-scale dataset. The performance improvement was obvious, however, they often fail to address inherent ambiguities of matching, such as textureless regions, repetitive patterns, and large displacements. To address this, we propose DiffMatch, a novel conditional diffusion-based framework designed to explicitly model both the data and prior terms. Unlike previous approaches, this is accomplished by leveraging a conditional denoising diffusion model. DiffMatch consists of two main components: conditional denoising diffusion module and cost injection module. We stabilize the training process and reduce memory usage with a stage-wise training strategy. Furthermore, to boost performance, we introduce an inference technique that finds a better path to the accurate matching field. Our experimental results demonstrate significant performance improvements of our method over existing approaches, and the ablation studies validate our design choices along with the effectiveness of each component. Project page is available at https://ku-cvlab.github.io/DiffMatch/.

cs.CV↗

arXiv2023

GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising

As quantum chemical properties have a dependence on their geometries, graph neural networks (GNNs) using 3D geometric information have achieved high prediction accuracy in many tasks. However, they often require 3D geometries obtained from high-level quantum mechanical calculations, which are practically infeasible, limiting their applicability to real-world problems. To tackle this, we propose a new training framework, GeoTMI, that employs denoising process to predict properties accurately using easy-to-obtain geometries (corrupted versions of correct geometries, such as those obtained from low-level calculations). Our starting point was the idea that the correct geometry is the best description of the target property. Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property. GeoTMI also explicitly updates the corrupted input to approach the correct geometry as it passes through the GNN layers, contributing to more effective denoising. We investigated the performance of the proposed method using 3D GNNs for three prediction tasks: molecular properties, a chemical reaction property, and relaxed energy in a heterogeneous catalytic system. Our results showed consistent improvements in accuracy across various tasks, demonstrating the effectiveness and robustness of GeoTMI.

physics.chem-ph↗