D-Wave NetworkX provides tools for working with Chimera and Pegasus graphs and implementations of graph-theory algorithms on the D-Wave system and other binary quadratic model samplers; for example, functions such as draw_chimera() provide easy visualization for Chimera graphs; functions such as maximum_cut() or min_vertex_cover() provide graph algorithms useful to optimization problems that fit well with the D-Wave system.
Like the D-Wave system, 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.
For an introduction to quantum processing unit (QPU) topologies such as the Chimera` and Pegasus graphs, see Topology.
For an introduction to binary quadratic models (BQMs), see Binary Quadratic Models.
For an introduction to samplers, see Samplers and Composites.
Below you can see how to create Chimera graphs implemented in the D-Wave 2X and D-Wave 2000Q systems:
import dwave_networkx as dnx # D-Wave 2X C = dnx.chimera_graph(12, 12, 4) # D-Wave 2000Q C = dnx.chimera_graph(16, 16, 4)