ML-QLS: Multilevel Quantum Layout Synthesis
Wan-Hsuan Lin, Jason Cong · 2025
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, optimal OLS tools face scalability issues, while heuristic methods suffer significant optimality gap due to the lack of global optimization. To address these challenges, we introduce a multilevel framework, which is an effective methodology for solving 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. The experimental results demonstrate that ML-QLS can scale up to problems involving hundreds of qubits and achieve a remarkable 69% performance improvement over leading heuristic QLS tools for large circuits, which underscores the effectiveness of multilevel frameworks in quantum applications.