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Engineering of the resistive switching properties in V2O5 thin film by atomic structural transition: Experiment and theory

Published in Journal of Applied Physics, 2018

We investigate the resistive switching properties in V2O5 thin films through atomic structural transitions, combining experimental observations with theoretical analysis. This work demonstrates how material engineering at the atomic level can control and optimize resistive switching behavior for memory applications.

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Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization

Published in IEEE International Conference on Quantum Computing and Engineering (QCE), 2025

We describe Agent-Q, an LLM fine-tuning system to generate and optimize quantum circuits. Agent-Q provides 14,000 quantum circuits covering 12 optimization problem instances and their optimized QAOA, VQE, and adaptive VQE circuits. Experimental results show superior performance of Agent-Q, compared to several state-of-the-art LLMs. Presented at IEEE QCE 2025.

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SHARP: Shared State Reduction for Efficient Matching of Sequential Patterns

Published in arXiv preprint, 2025

We present SHARP, a library that employs state reduction to achieve efficient best-effort pattern matching for complex event processing (CEP), OLAP, and retrieval-augmented generation (RAG). SHARP achieves a recall of 97%, 96% and 73% for pattern matching in CEP, OLAP, and RAG applications, under a bound of 50% of the average processing latency.

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QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL

Published in arXiv preprint, 2025

Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. We propose QUASAR, an agentic reinforcement learning (RL) framework for quantum circuits generation and optimization based on tool-augmented LLMs. When augmenting a 4B LLM, QUASAR has achieved the validity of 99.31% in Pass@1 and 100% in Pass@10, outperforming industrial LLMs of GPT-4o, GPT-5 and DeepSeek-V3.

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