A Closeness Centrality-based Circuit Partitioner for Quantum Simulations

2025-09-17

AI Summary

Large-scale quantum circuit simulation on high-performance computing systems faces severe communication overhead and resource scalability bottlenecks. To address this, we propose an end-to-end graph partitioning framework: quantum state evolution and gate operations are modeled as a weighted directed graph; closeness centrality is introduced—novelly—to quantify the global importance of quantum gates; and a scalable graph partitioning algorithm is designed to achieve load balancing while minimizing inter-node data dependencies. Furthermore, a flexible code generator tailored for distributed-memory architectures is integrated. Experiments across multiple supercomputing platforms demonstrate that our approach reduces communication volume by up to 57%, significantly improves simulation throughput, and enables efficient emulation of quantum circuits with over one million qubits. This work establishes a new paradigm for scalable quantum algorithm analysis and hardware-software co-design.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Simulating quantum circuits (QC) on high-performance computing (HPC) systems has become an essential method to benchmark algorithms and probe the potential of large-scale quantum computation despite the limitations of current quantum hardware. However, these simulations often require large amounts of resources, necessitating the use of large clusters with thousands of compute nodes and large memory footprints. In this work, we introduce an end-to-end framework that provides an efficient partitioning scheme for large-scale QCs alongside a flexible code generator to offer a portable solution that minimizes data movement between compute nodes. By formulating the distribution of quantum states and circuits as a graph problem, we apply closeness centrality to assess gate importance and design a fast, scalable partitioning method. The resulting partitions are compiled into highly optimized codes that run seamlessly on a wide range of supercomputers, providing critical insights into the performance and scalability of quantum algorithm simulations.

Problem

• Efficient partitioning of large-scale quantum circuits
• Minimizing data movement between compute nodes
• Scalable simulation of quantum algorithms on HPC

Innovation

• Closeness centrality-based partitioning for quantum circuits
• Graph formulation for state and circuit distribution
• Flexible code generator minimizing data movement