dwave-graphs
A package providing graphs and algorithms for working with D-Wave quantum computers.
Decision gist · record as of 2026-08-14
Yes, if you are working with D-Wave quantum computers or researching quantum annealing. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It is a specialized tool with a narrow audience—install only if you need QPU topology graphs or D-Wave-specific graph algorithms.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (3.14.1 excluded); depends on dimod, networkx, and numpy being installed.
- Low install friction with a pure Python wheel and three common dependencies (dimod, networkx, numpy).
- Active maintenance with a recent release and no known vulnerabilities.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for D-Wave's open-source quantum software ecosystem.
last release 2026-06-11 (64 days) · last repo commit 2026-06-16 · 95 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 77,527 downloads/mo, #14,518 on PyPI
Alternatives
Verify before relying
pip install dwave-graphs
import dwave.graphs
graph = dwave.graphs.pegasus_graph(16)- Whether pegasus_graph(16) is the correct size parameter for Advantage QPUs or if other sizes are commonly used.
- What specific graph-theory algorithms beyond topology representation are included in the package.
- Performance characteristics when working with large-scale QPU topologies.
What it is and what it does
dwave-graphs is a Python library for representing and manipulating quantum processor topologies, particularly the Pegasus topology used by D-Wave's Advantage quantum computers. It wraps graph structures and provides algorithms designed to work with binary quadratic model (BQM) samplers on D-Wave hardware and other compatible systems.
The package sits in the D-Wave Ocean SDK ecosystem and depends on networkx for graph operations, numpy for numerical work, and dimod for BQM handling. It is primarily useful for researchers and developers building quantum annealing applications who need to understand or work directly with QPU connectivity constraints, or who are implementing graph-based algorithms for quantum optimization.
Use it for
- Generate and inspect Pegasus topology graphs to understand D-Wave Advantage QPU connectivity constraints.
- Implement graph-theory algorithms optimized for quantum annealing on D-Wave hardware.
- Map classical optimization problems to QPU topology when designing BQM formulations.
- Prototype quantum annealing solutions that must respect hardware topology limitations.
- Research quantum processor architecture and topology-aware algorithm design.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working with D-Wave quantum computers or researching quantum annealing.
The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It is a specialized tool with a narrow audience—install only if you need QPU topology graphs or D-Wave-specific graph algorithms.
Install
dwave-graphs on PyPI
Before you install
Low install friction with a pure Python wheel and three common dependencies (dimod, networkx, numpy). Active maintenance with a recent release and no known vulnerabilities.
Requires Python 3.10 or later (3.14.1 excluded); depends on dimod, networkx, and numpy being installed.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for D-Wave's open-source quantum software ecosystem.
Quickstart
pip install dwave-graphs
import dwave.graphs
graph = dwave.graphs.pegasus_graph(16)Verify before relying
- Whether pegasus_graph(16) is the correct size parameter for Advantage QPUs or if other sizes are commonly used.
- What specific graph-theory algorithms beyond topology representation are included in the package.
- Performance characteristics when working with large-scale QPU topologies.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release !=3.14.1,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesdimodnetworkxnumpy |
| Maintenance | Actively maintained 64 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 77,527 / month, #14,518 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: dwave_graphs-1.0.0-py3-none-any.whl
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