用扩散模型统一初始化量子算法参数，提升训练效率。
DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models
- 将参数初始化转化为生成建模问题，采用扩散模型实现跨任务通用初始化。
- 在15,085个实例上实验，初始损失降低最多8.95，收敛步数减少23.4%。
- 适用于多任务、大规模量子算法，尤其适合研究高效初始化的学者。
变分量子算法（VQAs）在噪声中等规模量子（NISQ）时代广泛应用，但其可训练性和性能高度依赖于初始化参数，而这些参数决定了优化景观。现有基于机器学习的初始化方法虽达当前最优，但仅限于单任务场景和仅数百样本的小数据集。本文将VQA参数初始化重构为生成建模问题，提出基于去噪扩散概率模型（DDPM）的DiffQ初始化器。为支持稳健训练与评估，构建了包含三个领域、五个代表性任务的15,085个实例数据集。实验表明，DiffQ优于基线方法，初始损失最高降低8.95，收敛步数最多减少23.4%。
原文摘要 · Abstract (English)
Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization parameters that shape the optimization landscape. Existing machine learning-based initializers achieve state-of-the-art results yet remain constrained to single-task domains and small datasets of only hundreds of samples. We address these limitations by reformulating VQA parameter initialization as a generative modeling problem and introducing DiffQ, a parameter initializer based on the Denoising Diffusion Probabilistic Model (DDPM). To support robust training and evaluation, we construct a dataset of 15,085 instances spanning three domains and five representative tasks. Experiments demonstrate that DiffQ surpasses baselines, reducing initial loss by up to 8.95 and convergence steps by up to 23.4%.
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