Static and Adaptive Quantum Circuits for Co-Design and Multi-threading Partitioning Approach

Quantum computing stands at the forefront of technological innovation, promising to revolutionize fields ranging from cryptography to material science by leveraging the unique properties of quantum mechanics. Central to the advancement of quantum computing is the development of efficient and scalable quantum circuits, which serve as the fundamental building blocks for quantum algorithms. Traditional static quantum circuits, while powerful, often face limitations in flexibility and efficiency, particularly as the complexity of quantum algorithms increases.In recent years, adaptive quantum circuits have emerged as a compelling alternative to static circuits. Unlike their static counterparts, adaptive circuits possess the ability to dynamically adjust their structure and parameters in real-time based on intermediate measurement outcomes. This adaptability enhances both the flexibility and efficiency of quantum computations, enabling more responsive and optimized processing of quantum information. However, the dynamic nature of adaptive circuits introduces significant challenges in their design, optimization, and partitioning, necessitating novel approaches to effectively manage their complexity.A critical aspect of optimizing quantum circuits, whether static or adaptive, is the partitioning of circuits to minimize resource usage and maximize performance. Traditional partitioning techniques often fall short when applied to adaptive circuits due to their inherent dynamism and the intricate dependencies introduced by intermediate measurements. To address this, hypergraph representations have been proposed as a robust framework for modeling adaptive quantum circuits. In this representation, groups of quantum gates are encapsulated as hyperedges, allowing for a more nuanced depiction of gate interactions and dependencies. This extended hypergraph not only captures the structural intricacies of adaptive circuits but also integrates constraints that are pivotal during the partitioning process, ensuring that groups of ports associated with classical operations are preserved.Recognizing the need for benchmarks to evaluate and advance partitioning techniques for adaptive quantum circuits, this dataset introduces an initial set of benchmark quantum circuits. This dataset was curated to encompass a diverse array of quantum algorithms and configurations, tailored to assess both static and adaptive circuit approaches. By providing this dataset, we aim to share with the research community a valuable resource that facilitates the comparative analysis of different partitioning heuristics and optimization strategies. The availability of such standardized benchmarks is essential for driving forward the development of more efficient and effective quantum circuit methodologies.

量子计算（Quantum computing）位居技术创新前沿，凭借量子力学的独特特性，有望彻底变革从密码学到材料科学等诸多领域。推动量子计算发展的核心，在于高效且可扩展的量子电路（quantum circuits）研发——这类电路是量子算法的基础构建单元。传统静态量子电路虽具备强大算力，但在灵活性与效率上往往存在局限，尤其随着量子算法复杂度提升，这一问题愈发凸显。

近年来，自适应量子电路（adaptive quantum circuits）作为静态电路的极具吸引力的替代方案应运而生。与静态电路不同，自适应电路可基于中间测量结果实时动态调整自身结构与参数。这种自适应能力可提升量子计算的灵活性与效率，实现对量子信息更具响应性且更优化的处理。不过，自适应电路的动态特性也为其设计、优化与划分带来了重大挑战，亟需全新方法来有效管控其复杂度。

无论是静态还是自适应量子电路，优化的关键环节之一都是电路划分，以最小化资源占用并最大化性能。传统划分技术由于自适应电路固有的动态性，以及中间测量带来的复杂依赖关系，在应用于自适应电路时往往难以奏效。为解决这一问题，研究者提出将超图（hypergraph）表示作为建模自适应量子电路的稳健框架。在此表示方式中，量子门（quantum gates）组被封装为超边（hyperedges），可更细致地刻画量子门之间的交互与依赖关系。这种扩展后的超图不仅能捕捉自适应电路的结构细节，还可整合划分过程中至关重要的约束条件，确保与经典操作相关的端口组得以保留。

鉴于亟需基准测试集来评估并推动自适应量子电路划分技术的发展，本数据集推出了首批基准量子电路集合。本数据集精心收录了多样化的量子算法与配置，旨在用于评估静态与自适应电路方案。通过公开此数据集，我们希望为研究社区提供一项宝贵资源，以助力不同划分启发式方法与优化策略的对比分析。此类标准化基准测试集的可用性，对于推动更高效、更实用的量子电路方法论发展至关重要。