用扩散模型自动生成可运行的量子线路，无需人工设计。
Q-Fusion: Diffusing Quantum Circuits
- 基于扩散模型与LayerDAG框架生成量子线路
- 生成结果100%有效，无非法电路
- 适合量子算法设计初学者和自动化研究者
量子计算在解决社会相关且计算复杂的难题方面具有巨大潜力。量子机器学习（QML）有望显著提升现有机器学习能力。然而，当前的噪声中等规模量子（NISQ）设备受限于量子比特数量和门操作次数，制约了其全部潜能。此外，量子算法的设计仍是一项耗时且依赖领域专业知识的任务。量子架构搜索（QAS）旨在通过自动生成新型量子线路来简化该过程，减少人工干预。本文提出一种基于扩散模型的方法，结合LayerDAG框架生成新量子线路。该方法不同于使用大语言模型（LLM）、强化学习（RL）、变分自编码器（VAE）等技术的其他方案。实验表明，所提模型能持续生成100%有效的量子线路输出。
原文摘要 · Abstract (English)
Quantum computing holds great potential for solving socially relevant and computationally complex problems. Furthermore, quantum machine learning (QML) promises to rapidly improve our current machine learning capabilities. However, current noisy intermediate-scale quantum (NISQ) devices are constrained by limitations in the number of qubits and gate counts, which hinder their full capabilities. Furthermore, the design of quantum algorithms remains a laborious task, requiring significant domain expertise and time. Quantum Architecture Search (QAS) aims to streamline this process by automatically generating novel quantum circuits, reducing the need for manual intervention. In this paper, we propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits. This method contrasts with other approaches that utilize large language models (LLMs), reinforcement learning (RL), variational autoencoders (VAE), and similar techniques. Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
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