arXiv subjects

Donghwi Kim

Publications and source records attributed to Donghwi Kim.

8 recordsLinked to original sources

arXiv2026

Compiling Chemical Knowledge into Executable Descriptors for Materials Prediction

Materials prediction depends critically on how scientific knowledge is represented, yet many governing considerations exist only as natural-language heuristics that conventional learners cannot use. We introduce CRISP, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an executable scalar descriptor supplied to a conventional learner. For positive-unlabeled inorganic-crystal synthesizability, CRISP outperformed expert-curated and generic structural representations under a shared learner and surpassed purpose-built synthesizability models, with its advantage most pronounced under structural-size and chemical-family shifts. Infrequently generated rules contributed complementary predictive information, showing that generation frequency does not determine utility. The same workflow yielded competitive representations for formation energy and ionic conductivity while revealing task-dependent limits for shear modulus, establishing a dataset-blind, auditable route from broad chemical knowledge to transferable computational representations.

cond-mat.mtrl-sci↗

arXiv2025

Molecular Properties in Quantum-Classical Auxiliary-Field Quantum Monte Carlo: Correlated Sampling with Application to Accurate Nuclear Forces

We extend correlated sampling from classical auxiliary-field quantum Monte Carlo to the quantum-classical (QC-AFQMC) framework, enabling accurate nuclear force computations crucial for geometry optimization and reaction dynamics. Stochastic electronic structure methods typically encounter prohibitive statistical noise when computing gradients via finite differences. To address this, our approach maximizes correlation between nearby geometries by synchronizing random number streams, aligning orbitals, using deterministic integral decompositions, and employing a consistent set of classical shadow measurements defined at a single reference geometry. Crucially, reusing this single, reference-defined shadow ensemble eliminates the need for additional quantum measurements at displaced geometries. Together, these methodological choices substantially reduce statistical variance in computed forces. We validate the method across hydrogen chains, confirming accuracy throughout varying correlation regimes, and demonstrate significant improvements over single-reference methods in force evaluations for N$_2$ and stretched linear H$_4$, particularly in strongly correlated regions where conventional coupled cluster approaches qualitatively fail. Orbital-optimized trial wave functions further boost accuracy for demanding cases such as stretched CO$_2$, without increasing quantum resource requirements. Finally, we apply our methodology to the MEA-CO$_2$ carbon capture reaction, employing quantum information metrics for active space selection and matchgate shadows for efficient overlap evaluations, establishing QC-AFQMC as a robust framework for exploring complex reaction pathways.

quant-ph↗

arXiv2025

End-to-End Demonstration of Quantum Generative Adversarial Networks for Steel Microstructure Image Augmentation on a Trapped-Ion Quantum Computer

Generative adversarial networks (GANs) are a machine learning technique capable of producing high-quality synthetic images. In the field of materials science, when a crystallographic dataset includes inadequate or difficult-to-obtain images, synthetic images can be used for image augmentation to mitigate data scarcity and streamline the preparation of datasets for high-throughput analysis. We integrate quantum computing with GANs into a hybrid quantum-classical GAN to generate complex 5-channel electron backscatter diffraction (EBSD) images of two distinct microstructure phases of steel. By training a quantum circuit at the input layer of a large classical Wasserstein GAN (WGAN) model, we mitigate mode collapse and achieve higher image quality compared to a baseline classical GAN. We generate images from both ferrite and bainite microstructure phases in an end-to-end workflow. With respect to maximum mean discrepancy score, we find that the hybrid quantum-classical WGAN improves over classical Bernoulli GANs in 70% of samples. As the quantum computer is part of the training procedure, our method has potential to scale to larger number of qubits. Our results indicate that the WGAN model based on the quantum circuit ansatz may be effectively leveraged to enhance the quality of synthetic EBSD images on both quantum simulators and actual quantum hardware.

quant-ph↗

arXiv2025

Weighted-Sum Energy Efficiency Maximization in User-Centric Uplink Cell-Free Massive MIMO

This paper introduces the weighted-sum energy efficiency (WSEE) as an advanced performance metric designed to represent the uplink energy efficiency (EE) of individual user equipment (UE) in a user-centric Cell-Free massive MIMO (CF-mMIMO) system more accurately. In a realistic user-centric CF-mMIMO context, each UE may exhibit distinct characteristics, such as maximum transmit power limits or specific minimum data rate requirements. By computing the EE of each UE independently and adjusting the weights accordingly, the system can accommodate these unique attributes, thus promoting energy-efficient operation. The uplink WSEE is formulated as a multiple-ratio fractional programming (FP) problem, representing a weighted sum of the EE of individual UEs, which depends on each UE's transmit power and the combining vector at the central processing unit (CPU). To effectively maximize WSEE, we develop optimization algorithms based on the quadratic transform (QT), which is effective for multiple-ratio FP. By applying QT sequentially to each user's EE and the uplink SINR, the method converts the nonconvex WSEE objective into tractable subproblems and ensures stable, monotone convergence. We further introduce an approximate variant that alleviates QT's inherent nonlinearities to accelerate convergence. Compared with global energy efficiency (GEE)-oriented baselines, the proposed algorithms yield simultaneous improvements in user power consumption and spectral efficiency, while also reducing optimization time. Overall, the framework provides a foundation for designing operational strategies tailored to specific system requirements.

eess.SP↗

arXiv2024

Distribution-Adaptive Dynamic Shot Optimization for Variational Quantum Algorithms

Variational quantum algorithms (VQAs) have attracted remarkable interest over the past few years because of their potential computational advantages on near-term quantum devices. They leverage a hybrid approach that integrates classical and quantum computing resources to solve high-dimensional problems that are challenging for classical approaches alone. In the training process of variational circuits, constructing an accurate probability distribution for each epoch is not always necessary, creating opportunities to reduce computational costs through shot reduction. However, existing shot-allocation methods that capitalize on this potential often lack adaptive feedback or are tied to specific classical optimizers, which limits their applicability to common VQAs and broader optimization techniques. Our observations indicate that the information entropy of a quantum circuit's output distribution exhibits an approximately exponential relationship with the number of shots needed to achieve a target Hellinger distance. In this work, we propose a distribution-adaptive dynamic shot (DDS) framework that efficiently adjusts the number of shots per iteration in VQAs using the entropy distribution from the prior training epoch. Our results demonstrate that the DDS framework sustains inference accuracy while achieving a ~50% reduction in average shot count compared to fixed-shot training, and ~60% higher accuracy than recently proposed tiered shot allocation methods. Furthermore, in noisy simulations that reflect the error rates of actual IBM quantum systems, DDS achieves approximately a ~30% reduction in the total number of shots compared to the fixed-shot method with minimal degradation in accuracy, and offers about ~70% higher computational accuracy than tiered shot allocation methods.

quant-ph↗

arXiv2024

Recover as It is Designed to Be: Recovering from Compatibility Mobile App Crashes by Reusing User Flows

Android OS is severely fragmented by API updates and device vendors' OS customization, creating a market condition where vastly different OS versions coexist. This gives rise to compatibility crash problems where Android apps crash on certain Android versions but not on others. Although well-known, this problem is extremely challenging for app developers to overcome due to the sheer number of Android versions in the market that must be tested. We present RecoFlow, a framework for enabling app developers to automatically recover an app from a crash by programming user flows with our API and visual tools. RecoFlow tracks app feature usage with the user flows on user devices and recovers an app from a crash by replaying UI actions of the app feature disrupted by the crash. To prevent recurring compatibility crashes, RecoFlow executes a previously crashed app in compatibility mode that is enabled by our novel Android OS virtualization technique. Our evaluation with professional Android developers shows that our API and tools are easy to use and effective in recovering from compatibility crashes.

cs.HC↗

arXiv2024

Design Opportunities for Explainable AI Paraphrasing Tools: A User Study with Non-native English Speakers

We investigate how non-native English speakers (NNESs) interact with diverse information aids to assess and select AI-generated paraphrases. We develop ParaScope, an AI paraphrasing assistant that integrates diverse information aids, such as back-translation, explanations, and usage examples, and logs user interaction data. Our in-lab study with 22 NNESs reveals that user preferences for information aids vary by language proficiency, with workflows progressing from global to more detailed information. While back-translation was the most frequently used aid, it was not a decisive factor in suggestion acceptance; users combined multiple information aids to make informed decisions. Our findings demonstrate the potential of explainable AI paraphrasing tools to enhance NNESs' confidence, autonomy, and writing efficiency, while also emphasizing the importance of thoughtful design to prevent information overload. Based on these findings, we offer design implications for explainable AI paraphrasing tools that support NNESs in making informed decisions when using AI writing systems.

cs.HC↗

arXiv2023

Towards Explainable AI Writing Assistants for Non-native English Speakers

We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text. Through an interview study with 15 non-native English speakers (NNESs) with varying levels of English proficiency, we observe that they face difficulties in assessing paraphrased texts generated by AI writing assistants, largely due to the lack of explanations accompanying the suggested paraphrases. Furthermore, we examine their strategies to assess AI-generated texts in the absence of such explanations. Drawing on the needs of NNESs identified in our interview, we propose four potential user interfaces to enhance the writing experience of NNESs using AI writing assistants. The proposed designs focus on incorporating explanations to better support NNESs in understanding and evaluating the AI-generated paraphrasing suggestions.

cs.CL↗