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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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About me

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

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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talks

teaching