Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing

TL;DR

This paper introduces a novel compilation framework for distributed quantum computing that leverages circuit patterns, clustering, and annealing to optimize qubit mapping, significantly improving performance in heterogeneous systems.

Contribution

It presents a comprehensive approach combining pattern exploitation, clustering, and annealing for efficient quantum program compilation on heterogeneous distributed systems.

Findings

Reduces objective value by up to 88.40% compared to baseline

Effectively handles complex heterogeneous quantum systems

Demonstrates significant performance improvements

Abstract

Efficiently mapping quantum programs onto Distributed quantum computing (DQC) are challenging, particularly when considering the heterogeneous quantum processing units (QPUs) with different structures. In this paper, we present a comprehensive compilation framework that addresses these challenges with three key insights: exploiting structural patterns within quantum circuits, using clustering for initial qubit placement, and adjusting qubit mapping with annealing algorithms. Experimental results demonstrate the effectiveness of our methods and the capability to handle complex heterogeneous distributed quantum systems. Our evaluation shows that our method reduces the objective value at most 88.40\% compared to the baseline.