VQE-generated Quantum Circuit Dataset

VQE-generated Quantum Circuit Dataset是由大阪大学工程科学研究生院创建的一个量子电路数据集，包含1800条通过变分量子本征求解器（VQE）优化的量子电路数据。数据集涵盖了从4到20个量子位的范围，使用六种常见的凝聚态物理哈密顿量，并应用了十种不同深度的ansatz来生成六种不同类别的数据，每类包含300个样本。该数据集旨在通过量子方法进行有效的聚类和分类，特别适用于云量子计算机提供商分析用户提交的电路数据，以理解用户偏好。数据集的创建过程涉及使用VQE算法优化各种ansatz以生成不同哈密顿量的基态，从而构建一个具有良好分类特性的数据集。该数据集的应用领域主要集中在量子机器学习，特别是在解决量子数据处理问题中，预计量子方法将优于传统方法。VQE-generated Quantum Circuit Dataset is a quantum circuit dataset developed by the Graduate School of Engineering Science, Osaka University. It contains 1800 quantum circuit samples optimized via the Variational Quantum Eigensolver (VQE). The dataset covers quantum systems with 4 to 20 qubits, utilizes six widely-used condensed matter physics Hamiltonians, and employs ten ansätze of varying depths to generate six distinct data categories, each comprising 300 samples. This dataset is designed for effective clustering and classification using quantum methods, and is particularly applicable for cloud quantum computing providers to analyze user-submitted circuit data to understand user preferences. The creation process of this dataset involves optimizing various ansätze with the VQE algorithm to generate the ground states of different Hamiltonians, thereby constructing a dataset with excellent classification characteristics. Its main application areas focus on quantum machine learning, especially in solving quantum data processing problems, where quantum-based approaches are expected to outperform traditional methods.

搜集汇总

背景与挑战

背景概述

该数据集由大阪大学工程科学研究生院创建，包含1800条通过变分量子本征求解器（VQE）优化的量子电路数据，覆盖4到20个量子位，基于六种凝聚态物理哈密顿量和十种不同深度ansatz生成六类样本。它旨在支持量子方法进行有效的聚类和分类，帮助云量子计算机提供商分析用户电路以理解偏好，主要应用于量子机器学习领域，预计在量子数据处理中优于传统方法。