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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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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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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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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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Graduate Course, University of Southern California, Department of Electrical and Computer Engineering, 2019
Grading student assignments, exam papers and answering relevant questions on Piazza.