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.

翻译：量子计算机在量子机器学习和量子信号处理等众多应用领域的重要性日益凸显。这些应用在将经典数据集加载至量子存储器方面面临重大挑战。现有算法众多，且需考量多项质量属性，使得数据加载方法的比较变得复杂。本研究旨在（以结构化方式）比较将经典数据集加载至量子存储器（通过将态矢量转换为量子线路）的各种算法。我们基于五个关键属性评估态制备算法：线路深度、量子比特数、经典运行时间、态矢量表示形式（稠密或稀疏）以及线路可修改性。采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。为提升理解效率并加速比较过程，我们还对三个指标（即线路深度、量子比特数和经典运行时间）进行了可视化对比。我们比较了七种稠密态矢量转换算法和六种稀疏态矢量转换算法。分析将初始算法集缩减为两组稠密算法和两组稀疏算法，揭示了其固有的权衡关系。该比较方法为基于特定需求选择算法提供了结构化框架。研究人员和从业者可借此为不同量子计算任务选择适用的数据加载算法。