# UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis

arXiv: https://arxiv.org/abs/2501.16380 · HTML: https://mlsanity.com/paper/2501.16380/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Leveraging Diffusion Models for Parameterized Quantum Circuit Generation](https://mlsanity.com/paper/2505.20863/) (2025)
- [Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis](https://mlsanity.com/paper/2602.15146/) (2026)
- [Generative AI for Quantum Circuits and Quantum Code: A Technical Review and Taxonomy](https://mlsanity.com/paper/2603.16216/) (2026)
- [From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data](https://mlsanity.com/paper/2605.01367/) (2026)

## Abstract (arXiv)

Quantum computing is a transformative technology with wide-ranging applications, and efficient quantum circuit generation is crucial for unlocking its full potential. Current diffusion model approaches based on U-Net architectures, while promising, encounter challenges related to computational efficiency and modeling global context. To address these issues, we propose UDiT,a novel U-Net-style Diffusion Transformer architecture, which combines U-Net's strengths in multi-scale feature extraction with the Transformer's ability to model global context. We demonstrate the framework's effectiveness on two tasks: entanglement generation and unitary compilation, where UDiTQC consistently outperforms existing methods. Additionally, our framework supports tasks such as masking and editing circuits to meet specific physical property requirements. This dual advancement, improving quantum circuit synthesis and refining generative model architectures, marks a significant milestone in the convergence of quantum computing and machine learning research.