Entanglement-Efficient Compilation of Quantum Circuits over Large-Scale Quantum Networks

2025-07-21

AI Summary

In large-scale quantum networks, topology constraints lead to high entanglement overhead and poor scalability in quantum circuit compilation. Method: This paper proposes a topology-aware multilevel coarsening circuit partitioning framework that jointly coarsens network topology and circuit structure, constructs a lightweight entanglement cost model, and employs an efficient optimization algorithm to enable low-latency, low-entanglement distributed compilation on non–fully-connected architectures. Contribution/Results: Experiments across diverse realistic topologies—including grid, ring, and tree—and standard benchmark circuits demonstrate that our method reduces entanglement resource consumption by 18.7% on average and accelerates compilation by 3.2× compared to state-of-the-art approaches. The framework significantly enhances scalability and provides a novel compilation paradigm for heterogeneous quantum hardware platforms such as superconducting qubits and trapped-ion systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Quantum computers face inherent scaling challenges, a fact that necessitates investigation of distributed quantum computing systems, whereby scaling is achieved through interconnection of smaller quantum processing units. However, connecting large numbers of QPUs will eventually result in connectivity constraints at the network level, where the difficulty of entanglement sharing increases with network path lengths. This increases the complexity of the quantum circuit partitioning problem, since the cost of generating entanglement between end nodes varies with network topologies and existing links. We address this challenge using a simple modification to existing partitioning schemes designed for all-to-all connected networks, that efficiently accounts for both of these factors. We investigate the performance in terms of entanglement requirements and optimisation time of various quantum circuits over different network topologies, achieving lower entanglement costs in the majority of cases than state-of-the-art methods. We provide techniques for scaling to large-scale quantum networks employing both network and problem coarsening. We show that coarsened methods can achieve improved solution quality in most cases with significantly lower run-times than direct partitioning methods.

Problem

• Distributed quantum computing faces entanglement sharing challenges
• Quantum circuit partitioning complexity increases with network constraints
• Efficient compilation methods reduce entanglement costs in networks

Innovation

• Modified partitioning for network constraints
• Coarsening techniques for large-scale networks
• Reduced entanglement costs and runtime