热启动与量子计算：一项系统性映射研究
Warm-Starting and Quantum Computing: A Systematic Mapping Study
AI总结：
针对NISQ计算机的限制，该研究系统性映射量子计算中的热启动技术，识别其属性并进行可扩展分类，为领域提供见解并助力技术应用与后续研究。
摘要：
由于量子比特数量少且易出错，噪声中等规模量子（NISQ）计算机对其能成功执行的量子算法规模施加了限制。最先进的研究引入了多种技术来解决这些限制，其方式是利用已知或低成本生成的近似值、解决方案或模型作为处理任务的起点，而非从零开始。这些所谓的热启动技术旨在减少量子资源消耗，从而促进适合NISQ计算机能力的算法设计。在这项工作中，我们收集并分析了量子计算领域中关于热启动技术的科学文献。具体而言，我们（i）使用系统性映射研究的既定指南创建最先进热启动技术的系统性图谱；（ii）识别此类技术的相关属性；（iii）基于这些属性，以可扩展的分类方案对文献中确定的技术进行分类。我们的结果为该研究领域提供了见解，旨在帮助量子软件工程师对热启动技术进行分类并在实践中应用它们。此外，我们的贡献可作为热启动主题进一步研究的起点，因为它们提供了现有工作的概述并促进了研究空白的识别。
英文摘要：
Due to low numbers of qubits and their error-proneness, Noisy Intermediate-Scale Quantum (NISQ) computers impose constraints on the size of quantum algorithms they can successfully execute. State-of-the-art research introduces various techniques addressing these limitations by utilizing known or inexpensively generated approximations, solutions, or models as a starting point to approach a task instead of starting from scratch. These so-called warm-starting techniques aim to reduce quantum resource consumption, thus facilitating the design of algorithms suiting the capabilities of NISQ computers. In this work, we collect and analyze scientific literature on warm-starting techniques in the quantum computing domain. In particular, we (i) create a systematic map of state-of-the-art research on warm-starting techniques using established guidelines for systematic mapping studies, (ii) identify relevant properties of such techniques, and (iii) based on these properties classify the techniques identified in the literature in an extensible classification scheme. Our results provide insights into the research field and aim to help quantum software engineers to categorize warm-starting techniques and apply them in practice. Moreover, our contributions may serve as a starting point for further research on the warm-starting topic since they provide an overview of existing work and facilitate the identification of research gaps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
