D-Wave NetworkX provides tools for working with Quantum Processing Unit (QPU) topology graphs, such as the Pegasus used on the AdvantageTM system, and implementations of graph-theory algorithms on D-Wave quantum computers and other binary quadratic model samplers; for example, functions such as draw_pegasus() provide easy visualization for Pegasus graphs; functions such as maximum_cut() or min_vertex_cover() provide graph algorithms useful to optimization problems that fit well with D-Wave quantum computers.

Like D-Wave quantum computers, all other supported samplers must have sample_qubo and sample_ising methods for solving Ising and QUBO models and return an iterable of samples in order of increasing energy. You can set a default sampler using the set_default_sampler() function.


This example creates a Pegasus graph (used by Advantage) and a small Zephyr graph (used by the Advantage2TM prototype made available in LeapTM in June 2022):

>>> import dwave_networkx as dnx
>>> # Advantage
>>> P16 = dnx.pegasus_graph(16)
>>> # Advantage2
>>> Z4 = dnx.zephyr_graph(4)