As quantum computing technology advances, the complexity of quantum algorithms increases, necessitating a shift from low-level circuit descriptions to high-level programming paradigms. This paper addresses the challenges of developing a compilation algorithm that optimizes memory management and scales well for bigger, more complex circuits. Our approach models the high-level quantum code as a control flow graph and presents a workflow that searches for a topological sort that maximizes opportunities for qubit reuse. Various heuristics for qubit reuse strategies handle the trade-off between circuit width and depth. We also explore scalability issues in large circuits, suggesting methods to mitigate compilation bottlenecks. By analyzing the structure of the circuit, we are able to identify sub-problems that can be solved separately, without a significant effect on circuit quality, while reducing runtime significantly. This method lays the groundwork for future advancements in quantum programming and compiler optimization by incorporating scalability into quantum memory management.

翻译：随着量子计算技术的进步，量子算法的复杂性日益增加，亟需从低层次电路描述转向高层次编程范式。本文针对开发一种能够优化内存管理、并适应更大更复杂电路可扩展性的编译算法所面临的挑战展开研究。我们的方法将高层次量子代码建模为控制流图，并提出一种工作流程，通过搜索拓扑排序以最大化量子比特重用的机会。多种量子比特重用策略的启发式方法处理了电路宽度与深度之间的权衡关系。我们还探讨了大规模电路中的可扩展性问题，提出了缓解编译瓶颈的方法。通过分析电路结构，我们能够识别出可独立求解的子问题，这些子问题在几乎不影响电路质量的同时，显著减少了运行时间。该方法通过将可扩展性融入量子内存管理，为未来量子编程和编译器优化的进一步发展奠定了基础。