# Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy

arXiv: https://arxiv.org/abs/2603.16216 · HTML: https://mlsanity.com/paper/2603.16216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Quantum circuit synthesis with diffusion models](https://mlsanity.com/paper/2311.02041/) (2023)
- [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://mlsanity.com/paper/2406.14712/) (2024)
- [Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization](https://mlsanity.com/paper/2504.11109/) (2025)
- [QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design](https://mlsanity.com/paper/2410.07961/) (2024)
- [QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming](https://mlsanity.com/paper/2508.20134/) (2025)
- [QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL](https://mlsanity.com/paper/2510.00967/) (2025)
- [KetGPT - Dataset Augmentation of Quantum Circuits using Transformers](https://mlsanity.com/paper/2402.13352/) (2024)
- [Synthesis of discrete-continuous quantum circuits with multimodal diffusion models](https://mlsanity.com/paper/2506.01666/) (2025)
- [Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant](https://mlsanity.com/paper/2508.20907/) (2025)
- [UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis](https://mlsanity.com/paper/2501.16380/) (2025)
- [Q-Fusion: Diffusing Quantum Circuits](https://mlsanity.com/paper/2504.20794/) (2025)
- [Leveraging Diffusion Models for Parameterized Quantum Circuit Generation](https://mlsanity.com/paper/2505.20863/) (2025)
- [AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization](https://mlsanity.com/paper/2403.12979/) (2024)

## Abstract (arXiv)

We review thirteen generative systems and five supporting datasets for quantum circuit and quantum code generation, identified through a structured scoping review of Hugging Face, arXiv, and provenance tracing (January-February 2026). We organize the field along two axes: artifact type (Qiskit code, OpenQASM programs, circuit graphs); crossed with training regime (supervised fine-tuning, verifier-in-the-loop RL, diffusion/graph generation, agentic optimization); and systematically apply a three-layer evaluation framework covering syntactic validity, semantic correctness, and hardware executability. The central finding is that while all reviewed systems address syntax and most address semantics to some degree, none reports end-to-end evaluation on quantum hardware (Layer 3b), leaving a significant gap between generated circuits and practical deployment. Scope note: quantum code refers throughout to quantum program artifacts (QASM, Qiskit); we do not cover generation of quantum error-correcting codes (QEC).