SQuASH Surrogate Benchmark Dataset for Quantum Architecture Search (QAS)

This dataset supports the SQuASH benchmark for Quantum Architecture Search (QAS), as presented in our paper. It includes training and evaluation data used for surrogate model learning, structured into multiple problem instances. Each subdirectory contains a database file with information extracted from .pkl files, such as initial PQC, optimal PQC and target evaluation metric, e.g., fidelity or train/test accuracy.

The dataset is organized for direct integration with the SQuASH GitHub repository and is designed to accelerate QAS research and support reproducible benchmarking.

本数据集可用于支撑本论文中提出的面向量子架构搜索（Quantum Architecture Search, QAS）的SQuASH基准测试。其包含用于代理模型学习的训练与评估数据，并被整理为多个问题实例。每个子目录均包含一个从.pkl文件中提取信息得到的数据库文件，其中涵盖初始参数化量子电路（Parameterized Quantum Circuit, PQC）、最优参数化量子电路以及目标评估指标（如保真度或训练/测试准确率）等内容。
本数据集的组织形式可直接与SQuASH的GitHub代码库集成，旨在加速量子架构搜索相关研究，并支持可复现的基准测试工作。