rustworkx: A High-Performance Graph Library for Python

Abstract

In rustworkx, we provide a high-performance, flexible graph library for Python. rustworkx is inspired by NetworkX but addresses many performance concerns of the latter. rustworkx is written in Rust and is particularly suited for performance-sensitive applications that use graph representations.

References in corpus (3)

• Noisy intermediate-scale quantum (NISQ) algorithms
• OpenQASM 3: A broader and deeper quantum assembly language
• Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search

Cited by in corpus (6)

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• Benchmarking the performance of quantum computing software
• Fast Partitioning of Pauli Strings into Commuting Families for Optimal Expectation Value Measurements of Dense Operators
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• Network Operations Scheduling for Distributed Quantum Computing
• Efficient QAOA Architecture for Solving Multi-Constrained Optimization Problems