用Transformer改进量子电路生成，速度与质量双提升
UDiTQC: U-Net-Style Diffusion Transformer for Quantum Circuit Synthesis
- 融合U-Net与Transformer，兼顾多尺度特征与全局建模
- 在纠缠态生成和酉矩阵编译任务上超越现有方法
- 支持电路掩码与编辑，适配特定物理需求
量子计算是一项变革性技术，高效生成量子电路对其潜力释放至关重要。现有基于U-Net架构的扩散模型虽具前景，但在计算效率和全局上下文建模方面存在挑战。为此，我们提出UDiT——一种新型的类U-Net扩散Transformer架构，结合了U-Net在多尺度特征提取方面的优势与Transformer建模全局上下文的能力。我们在纠缠态生成和酉矩阵编译两个任务上验证了该框架的有效性，结果显示UDiTQC持续优于现有方法。此外，该框架还支持电路掩码与编辑，以满足特定物理属性要求。这一双重进展——既提升量子电路合成性能，又优化生成模型架构——标志着量子计算与机器学习研究融合的重要里程碑。
原文摘要 · Abstract (English)
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.
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