[论文解读] Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects

Yan Ge, Wu Wenjie+7 more|arXiv (Cornell University)|Jun 30, 2024

Quantum Computing Algorithms and Architecture被引用 9

一句话总结

对量子电路综合、优化与编译的全面综述，突出表示、方法，以及面向 NISQ 与容错阶段的 AI 驱动的集成设计方法。

ABSTRACT

Quantum computing is a promising paradigm that may overcome the current computational power bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies, e.g., quantum logic circuit synthesis (also widely known as quantum architecture search) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, it becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.

研究动机与目标

• 审查从量子算法设计到在量子硬件上可执行程序的端到端工作流程。
• 系统性地对电路表示、综合、优化和量子比特映射技术进行分类。
• 分析在 NISQ 和容错时代的现实约束，并提出集成的 AI 辅助设计与编译策略。
• 突出硬件云上自动化量子电路设计与编译的挑战、机遇与前景。

提出的方法

• 对量子电路表示（门模型、DAG、相多项式、ZX 图、张量网络）进行调查和分类学整理。
• 将综合方法分为针对单位变换的和非单位变换的问题的分类（如 oracle 构造、VQE、QNN、误差纠正）。
• 讨论优化目标与方法，包括模式匹配、小洞优化、基于数据库的子电路、强化学习以及基于机器学习的搜索。
• 在硬件连通性约束和 SWAP 闸代价下对量子比特映射与路由进行分析。
• 提出一个集成的量子电路设计与编译范式，利用 AI 实现自动化的硬件感知优化。

Figure 1 : Quantum algorithm implementation pipeline. A quantum algorithm can be written in the form of multiple unitary transformations. logic circuit synthesis and optimization methods are then applied to obtain logic circuits. We utilize qubit mapping and routing methods to build an executable pr

实验结果

研究问题

• RQ1 在量子电路综合与优化中，占主导地位的表示及其作用是什么？
• RQ2 如何将 AI/ML 技术整合到跨硬件平台的量子电路设计与编译的自动化中？
• RQ3 什么样的集成流程能够将电路设计、优化与量子比特映射衔接起来，在 NISQ 与容错约束下最大化硬件性能？

主要发现

• 量子电路表示（门模型、DAG、相多项式、ZX 图、张量网络）使得能够将综合与优化方法定制到电路结构上。
• 综合问题分为针对单位变换的（如 oracle 实现）和非单位变换的（如 VQE、QNN、误差纠正），具有不同的目标公式。
• 人工智能和机器学习方法（强化学习、神经网络结构搜索类方法）在发现高效电路及子电路方面展现出潜力，超越传统启发式。
• 由于连通性和 SWAP 闸成本，量子比特映射与路由仍然关键，促使进行综合优化以在准确性和噪声之间取得平衡。
• 该综述主张一个集成的量子电路设计与编译框架，利用 AI 在硬件感知约束下实现自动化和优化。

Figure 2 : Quantum Circuit Representation. Gate Model: most common representation method; DAG: example DAG transformed from the gate model example, “start” and “end” nodes are added to complete the graph; QMDD: QMDD representation of a three-qubit quantum operation, we present the transformation mat