A High-Performance Multilevel Framework for Quantum Layout Synthesis

TL;DR

ML-SABRE introduces a multilevel framework for quantum layout synthesis that significantly reduces SWAP overhead, circuit depth, and compilation time, improving scalability and solution quality for larger quantum circuits.

Contribution

It presents a hierarchical optimization approach using LightSABRE at all levels, achieving better performance and scalability than existing methods.

Findings

Decreases SWAP count by over 60%

Reduces circuit depth by 17%

Cuts compilation time by 60%

Abstract

Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We employ the state-of-the-art heuristic method, LightSABRE, at all levels to ensure both efficiency and performance. Our evaluation on real benchmarks and hardware architectures shows that ML-SABRE decreases SWAP count by over 60%, circuit depth by 17%, and delivers a 60% compilation time reduction compared to state-of-the-art solvers. Further optimality studies reveal that ML-SABRE can significantly reduce the optimality gap by up to 82% for SWAP count and 49% for circuit depth, making it well-suited for emerging quantum devices with increasing size and architectural complexity.