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

arXiv: https://arxiv.org/abs/2504.11109 · HTML: https://mlsanity.com/paper/2504.11109/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 37 of its 69 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence](https://mlsanity.com/paper/2406.11931/) (2024)
- [Generating Images with Multimodal Language Models](https://mlsanity.com/paper/2305.17216/) (2023)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [CodeGemma: Open Code Models Based on Gemma](https://mlsanity.com/paper/2406.11409/) (2024)
- [OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models](https://mlsanity.com/paper/2411.04905/) (2024)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://mlsanity.com/paper/2406.14712/) (2024)
- [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://mlsanity.com/paper/2405.19495/) (2024)

## Built on by (most cited first, 17 of 17)

- [QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL](https://mlsanity.com/paper/2510.00967/) (2025)
- [Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant](https://mlsanity.com/paper/2508.20907/) (2025)
- [QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging](https://mlsanity.com/paper/2606.07314/) (2026)
- [Quantum Circuit Generation via test-time learning with large language models](https://mlsanity.com/paper/2602.03466/) (2026)
- [Q-Bridge: Code Translation for Quantum Machine Learning via LLMs](https://mlsanity.com/paper/2603.27836/) (2026)
- [Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model](https://mlsanity.com/paper/2605.23934/) (2026)
- [Aligning Quantum Operators with Large Language Models](https://mlsanity.com/paper/2606.13811/) (2026)
- [EARL: Efficient Agentic Reinforcement Learning Systems for Large Language Models](https://mlsanity.com/paper/2510.05943/) (2025)
- [Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes](https://mlsanity.com/paper/2511.04243/) (2025)
- [Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy](https://mlsanity.com/paper/2603.16216/) (2026)
- [QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning](https://mlsanity.com/paper/2605.12365/) (2026)
- [QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits](https://mlsanity.com/paper/2605.30358/) (2026)
- [Fine-Tuning Large Language Models for Quantum Reasoning](https://mlsanity.com/paper/2606.21974/) (2026)
- [Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design](https://mlsanity.com/paper/2607.19506/) (2026)
- [LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams](https://mlsanity.com/paper/2609.05327/) (2026)
- [Conduit: An Experience Data Plane for Distributed Reinforcement Learning](https://mlsanity.com/paper/2609.24456/) (2026)
- [AG-CoT: Verified Algorithmic Traces for LLM Program Synthesis on Clifford Circuits](https://mlsanity.com/paper/2609.33192/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have achieved remarkable outcomes in complex problems, including math, coding, and analyzing large amounts of scientific reports. Yet, few works have explored the potential of LLMs in quantum computing. The most challenging problem is to leverage LLMs to automatically generate quantum circuits at a large scale. Fundamentally, the existing pre-trained LLMs lack the knowledge of quantum circuits. In this paper, we address this challenge by fine-tuning LLMs and injecting the domain-specific knowledge of quantum computing. We describe Agent-Q, an LLM fine-tuning system to generate and optimize quantum circuits. In particular, Agent-Q implements the mechanisms to generate training data sets and constructs an end-to-end pipeline to fine-tune pre-trained LLMs to generate parameterized quantum circuits for various optimization problems. Agent-Q provides 14,000 quantum circuits covering a large spectrum of the quantum optimization landscape: 12 optimization problem instances and their optimized QAOA, VQE, and adaptive VQE circuits. Based thereon, Agent-Q fine-tunes LLMs and constructs syntactically correct parametrized quantum circuits in OpenQASM 3.0. We have evaluated the quality of the LLM-generated circuits and parameters by comparing them to the optimized expectation values and distributions. Experimental results show superior performance of Agent-Q, compared to several state-of-the-art LLMs and better parameters than random. Agent-Q can be integrated into an agentic workflow, and the generated parametrized circuits with initial parameters can be used as a starting point for further optimization, e.g., as templates in quantum machine learning and as benchmarks for compilers and hardware.