标题： 将经典数据集加载到量子内存中的算法比较 显示英文标题

标题： Comparing Algorithms for Loading Classical Datasets into Quantum Memory

摘要： 量子计算机在量子机器学习和量子信号处理等应用中正变得越来越重要。 这些应用在将经典数据集加载到量子内存中面临重大挑战。 随着可用算法的增多和需要考虑的多个质量属性，比较数据加载方法变得复杂。 我们的目标是以结构化的方式比较各种将经典数据集加载到量子内存的算法（通过将状态向量转换为电路）。 我们根据五个关键属性对状态准备算法进行评估：电路深度、量子比特数量、经典运行时间、状态向量表示（密集或稀疏）以及电路可修改性。 我们使用帕累托集作为多目标优化工具，以识别具有最佳属性组合的算法。 为了提高理解程度并加快比较速度，我们还对三个指标（即电路深度、量子比特数量和经典运行时间）进行可视化比较。 我们比较了七种用于密集状态向量转换的算法和六种用于稀疏状态向量转换的算法。 我们的分析将初始算法集减少到两个密集组和两个稀疏组，突显了内在的权衡。 这种比较方法为根据特定需求选择算法提供了一种结构化的方法。 研究人员和实践者可以利用它来帮助选择适用于各种量子计算任务的数据加载算法。 显示英文摘要

摘要： Quantum computers are gaining importance in various applications like quantum machine learning and quantum signal processing. These applications face significant challenges in loading classical datasets into quantum memory. With numerous algorithms available and multiple quality attributes to consider, comparing data loading methods is complex. Our objective is to compare (in a structured manner) various algorithms for loading classical datasets into quantum memory (by converting statevectors to circuits). We evaluate state preparation algorithms based on five key attributes: circuit depth, qubit count, classical runtime, statevector representation (dense or sparse), and circuit alterability. We use the Pareto set as a multi-objective optimization tool to identify algorithms with the best combination of properties. To improve comprehension and speed up comparisons, we also visually compare three metrics (namely, circuit depth, qubit count, and classical runtime). We compare seven algorithms for dense statevector conversion and six for sparse statevector conversion. Our analysis reduces the initial set of algorithms to two dense and two sparse groups, highlighting inherent trade-offs. This comparison methodology offers a structured approach for selecting algorithms based on specific needs. Researchers and practitioners can use it to help select data-loading algorithms for various quantum computing tasks.