arXiv subjects

Yongju Lee

Publications and source records attributed to Yongju Lee.

5 recordsLinked to original sources

arXiv2026

A Cyclic Layerwise QAOA Training

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems. Multi-angle QAOA (MA-QAOA), which assigns independent parameters to each Hamiltonian operator term, achieves superior approximation performance even with fewer layers than standard QAOA. Unfortunately, this increased expressibility can raise the classical computational cost due to a greater number of parameters. The recently proposed Layerwise MA-QAOA (LMA-QAOA) reduces this overhead by training one layer at a time, but it may suffer from obtaining the precise solution due to the previously fixed parameters. This work addresses two questions for efficient MA-QAOA training: (i) What is the optimal granularity for parameter updates per epoch, and (ii) How can we get precise final cost function results while only partially updating the parameters per epoch? Despite the benefit of reducing the parameters that update per epoch can reduce the classical computation overhead, too fine or coarse a granularity of Hamiltonian update can degrade the MA-QAOA training efficiency. We find that optimizing one complete layer per epoch is an efficient granularity. Moreover, selectively retraining each layer by tracking gradient variations can achieve a final cost function equivalent to the standard MA-QAOA while lowering the parameter update overhead. Based on these insights, we propose Orbit-QAOA, which cyclically revisits layers and selectively freezes stabilized parameters. Across diverse graph benchmarks, Orbit-QAOA reduces training steps by up to 81.8%, reduces approximation ratio error by up to 72x compared to the unified stop condition-applied enhanced LMA-QAOA, and achieves equivalent approximation performance compared to the standard MA-QAOA.

quant-ph↗

arXiv2026

Plutarch: Toward Scalable Operational Parallelism on Racetrack-Shaped Trapped-Ion Processors

A recent advancement in quantum computing shows a quantum advantage of certified randomness on the racetrack processor. This work investigates the execution efficiency of this architecture for general-purpose programs. We first explore the impact of increasing zones on runtime efficiency. Counterintuitively, our evaluations using variational programs reveal that expanding zones may degrade runtime performance under the existing scheduling policy. This degradation may be attributed to the increase in track length, which increases ion circulation overhead, offsetting the benefits of enhanced parallelism. To mitigate this, the proposed \textit{Plutarch} exploits 3 strategies: (i) unitary decomposition and translation to maximize zone utilization, (ii) prioritizing the execution of nearby gates over ion circulation, and (iii) implementing shortcuts to provide the alternative path.

quant-ph↗

arXiv2025

Garibaldi: A Pairwise Instruction-Data Management for Enhancing Shared Last-Level Cache Performance in Server Workloads

Modern CPUs suffer from the frontend bottleneck because the instruction footprint of server workloads exceeds the private cache capacity. Prior works have examined the CPU components or private cache to improve the instruction hit rate. The large footprint leads to significant cache misses not only in the core and faster-level cache but also in the last-level cache (LLC). We observe that even with an advanced branch predictor and instruction prefetching techniques, a considerable amount of instruction accesses descend to the LLC. However, state-of-the-art LLC designs with elaborate data management overlook handling the instruction misses that precede corresponding data accesses. Specifically, when an instruction requiring numerous data accesses is missed, the frontend of a CPU should wait for the instruction fetch, regardless of how much data are present in the LLC. To preserve hot instructions in the LLC, we propose Garibaldi, a novel pairwise instruction-data management scheme. Garibaldi tracks the hotness of instruction accesses by coupling it with that of data accesses and adopts management techniques. On the one hand, this scheme includes a selective protection mechanism that prevents the cache evictions of high-cost instruction cachelines. On the other hand, in the case of unprotected instruction line misses, Garibaldi conservatively issues prefetch requests of the paired data lines while handling those misses. In our experiments, we evaluate Garibaldi with 16 server workloads on a 40-core machine. We also implement Garibaldi on top of a modern LLC design, including Mockingjay. Garibaldi improves 13.2% and 6.1% of CPU performance on baseline LLC design and Mockingjay, respectively.

cs.AR↗

arXiv2024

Using LLMs to Investigate Correlations of Conversational Follow-up Queries with User Satisfaction

With large language models (LLMs), conversational search engines shift how users retrieve information from the web by enabling natural conversations to express their search intents over multiple turns. Users' natural conversation embodies rich but implicit signals of users' search intents and evaluation of search results to understand user experience with the system. However, it is underexplored how and why users ask follow-up queries to continue conversations with conversational search engines and how the follow-up queries signal users' satisfaction. From qualitative analysis of 250 conversational turns from an in-lab user evaluation of Naver Cue:, a commercial conversational search engine, we propose a taxonomy of 18 users' follow-up query patterns from conversational search, comprising two major axes: (1) users' motivations behind continuing conversations (N = 7) and (2) actions of follow-up queries (N = 11). Compared to the existing literature on query reformulations, we uncovered a new set of motivations and actions behind follow-up queries, including asking for subjective opinions or providing natural language feedback on the engine's responses. To analyze conversational search logs with our taxonomy in a scalable and efficient manner, we built an LLM-powered classifier (73% accuracy). With our classifier, we analyzed 2,061 conversational tuples collected from real-world usage logs of Cue: and examined how the conversation patterns from our taxonomy correlates with satisfaction. Our initial findings suggest some signals of dissatisfactions, such as Clarifying Queries, Excluding Condition, and Substituting Condition with follow-up queries. We envision our approach could contribute to automated evaluation of conversation search experience by providing satisfaction signals and grounds for realistic user simulations.

cs.HC↗

arXiv2016

Ab initio Study of Aspirin Adsorption on Single-walled Carbon and Carbon Nitride Nanotubes

We use ab intio density functional theory to investigate the adsorption properties of acetylsalicylic acid or aspirin on a (10, 0) carbon nanotube (CNT) and a (8, 0) triazine-based graphitic carbon nitride nanotube (CNNT). It is found that an aspirin molecule binds stronger to the CNNT with its adsorption energy of 0.67 eV than to the CNT with 0.51 eV. The stronger adsorption energy on the CNNT is ascribed to the high reactivity of its N atoms with high electron affinity. The CNNT exhibits local electric dipole moments, which cause strong charge redistribution in the aspirin molecule adsorbed on the CNNT than on the CNT. We also explore the influence of an external electric field on the adsorption properties of aspirin on these nanotubes by examining the modifications in their electronic band structures, partial densities of states, and charge distributions. It is found that an electric field applied along a particular direction induces aspirin molecular states in the in-gap region of the CNNT implying a potential application of aspirin detection.

cond-mat.mtrl-sci↗