首个大规模量子变分算法初始化数据集，助力提升量子计算性能。
VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers
- 构建覆盖三大领域、七类任务的12110个实例数据集
- 包含完整哈密顿量、电路结构与优化轨迹信息
- 适合量子算法优化与机器学习辅助研究者使用
变分量子本征求解器（VQE）是当前噪声中等规模量子（NISQ）设备的核心算法之一，其性能高度依赖参数初始化。尽管基于机器学习的初始化方法已达到先进水平，但受限于缺乏全面的数据资源。现有数据集通常局限于单一领域，仅包含数百个样本，且在哈密顿量、量子线路结构及优化轨迹等方面覆盖不全。为此，我们推出VQEzy——首个大规模的VQE参数初始化数据集。该数据集涵盖三个主要领域和七项代表性任务，共包含12,110个实例，每个实例均提供完整的VQE配置信息和完整的优化轨迹。数据集已在线公开，将持续更新与扩展，以支持未来在VQE优化方面的研究。
原文摘要 · Abstract (English)
Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization. Although recent machine learning-based initialization methods have achieved state-of-the-art performance, their progress has been limited by the lack of comprehensive datasets. Existing resources are typically restricted to a single domain, contain only a few hundred instances, and lack complete coverage of Hamiltonians, ansatz circuits, and optimization trajectories. To overcome these limitations, we introduce VQEzy, the first large-scale dataset for VQE parameter initialization. VQEzy spans three major domains and seven representative tasks, comprising 12,110 instances with full VQE specifications and complete optimization trajectories. The dataset is available online, and will be continuously refined and expanded to support future research in VQE optimization.
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