{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Extends NetworkX with tools for D-Wave quantum computer topology graphs and implements graph algorithms for quantum processing units and binary quadratic model samplers.","skillfed_tags":["quantum-computing","graph-algorithms","deprecated"],"use_cases":["Generate and analyze Pegasus topology graphs for D-Wave Advantage quantum computers.","Implement graph algorithms optimized for quantum processing on D-Wave systems.","Convert classical graph problems into binary quadratic models for quantum sampling.","Explore quantum processor connectivity and topology constraints in research.","Prototype quantum-classical hybrid algorithms using graph-based problem formulations."],"what_it_does":"dwave-networkx is a NetworkX extension designed for users working with D-Wave quantum computers. It provides specialized tools for working with quantum processor unit (QPU) topology graphs\u2014such as the Pegasus topology used on Advantage quantum computers\u2014and implements graph-theory algorithms that run on D-Wave quantum systems and other binary quadratic model samplers. The package builds on NetworkX's graph exploration and analysis capabilities, adding quantum-specific functionality.\n\nThe package is actively maintained but officially deprecated in favor of dwave-graphs, which provides the same core functionality under a new namespace (dwave.graphs). It has low installation friction as a pure-Python package and supports modern Python versions (3.10\u20133.14). Its three runtime dependencies\u2014NetworkX, dimod, and NumPy\u2014are widely used and stable.","worth_installing":"Yes, but with a caveat: the package is actively maintained and suitable for working with D-Wave quantum systems, but it is officially deprecated in favor of dwave-graphs. Install this only if you have an existing codebase using dwave-networkx or are specifically targeting its current API; for new projects, use dwave-graphs instead. No security vulnerabilities are known."},"id":"dwave-networkx","links":{"html":"https://skillfed.io/packages/dwave-networkx","md":"https://skillfed.io/packages/dwave-networkx.md","pypi":"https://pypi.org/project/dwave-networkx/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-16","license_spdx":null,"license_treatment":"permissive","name":"dwave-networkx","python_support":"supports_current","summary":"A NetworkX extension providing graphs and algorithms relevant to working with the D-Wave System"},"popularity":{"monthly_downloads":95579,"position":13262,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.19"}