Quantum Layout Synthesis (QLS) plays a crucial role in optimizing quantum circuit execution on physical quantum devices. As we enter the era where quantum computers have hundreds of qubits, we are faced with scalability issues using optimal approaches and degrading heuristic methods' performance due to the lack of global optimization. To this end, we introduce a hybrid design that obtains the much improved solution for the heuristic method utilizing the multilevel framework, which is an effective methodology to solve large-scale problems in VLSI design. In this paper, we present ML-QLS, the first multilevel quantum layout tool with a scalable refinement operation integrated with novel cost functions and clustering strategies. Our clustering provides valuable insights into generating a proper problem approximation for quantum circuits and devices. Our experimental results demonstrate that ML-QLS can scale up to problems involving hundreds of qubits and achieve a remarkable 52% performance improvement over leading heuristic QLS tools for large circuits, which underscores the effectiveness of multilevel frameworks in quantum applications.

翻译：量子布局综合（QLS）在优化量子电路于物理量子设备上的执行中起着至关重要的作用。随着我们进入量子计算机拥有数百个量子比特的时代，我们面临着最优方法带来的可扩展性问题，以及启发式方法因缺乏全局优化而导致的性能下降。为此，我们引入了一种混合设计，该设计利用多层级框架为启发式方法获得了显著改进的解，该框架是解决超大规模集成电路（VLSI）设计中大规模问题的有效方法。本文提出了ML-QLS，这是首个多层级量子布局工具，它集成了可扩展的细化操作以及新颖的成本函数和聚类策略。我们的聚类方法为生成量子电路与设备的合适问题近似提供了有价值的见解。实验结果表明，ML-QLS能够扩展到涉及数百个量子比特的问题，并且对于大型电路，其性能相比领先的启发式QLS工具实现了高达52%的显著提升，这突显了多层级框架在量子应用中的有效性。