Communication-Aware Qubit Placement and Automatic Node-Count Allocation for Distributed State-Vector Simulation

2026-10-07

AI Summary

This study addresses the excessive inter-node communication overhead caused by inefficient qubit mapping in distributed quantum simulation, which severely limits system performance and scalability. We propose a communication-aware qubit mapping optimization framework that, for the first time, integrates mapping optimization with automatic allocation of compute nodes. Supporting dynamic resource scaling and OpenQASM parsing, the framework significantly reduces cross-node communication cuts through strategic qubit placement. It has been integrated into the QuCSLO library and combined with the QuEST simulator to achieve system-level optimization. Experimental results demonstrate that, compared to identity mapping, our approach effectively reduces communication volume and achieves over 2× runtime speedup, validating the critical role of strategic qubit placement for highly entangled circuits.

Abstract

Distributed quantum circuit simulation enables the execution of large-scale quantum algorithms by partitioning qubits across multiple computational nodes. However, inefficient qubit placement frequently leads to excessive inter-node communication, severely limiting performance and scalability. This work introduces a communication-aware qubit layout optimization framework that systematically minimizes data exchange among nodes while dynamically selecting the optimal number of computational resources. Integrated into the QuCSLO library, our approach supports dynamic node scaling, OpenQASM parsing, and seamless integration with the QuEST simulator. Extensive evaluations on both synthetic benchmarks and real-world QASMBench circuits demonstrate that optimized layouts significantly reduce communication cuts and yield runtime speedups of over 2x compared to identity layouts, particularly in highly entangled circuits. Our results underscore the critical impact of strategic qubit placement on distributed simulation efficiency and provide a practical, resource-scalable solution for quantum workloads.

Problem

• Distributed quantum simulation
• Qubit placement
• Inter-node communication
• Scalability
• Node-count allocation

Innovation

• Distributed quantum simulation
• Communication-aware qubit placement
• Dynamic node allocation
• State-vector simulation
• Layout optimization