# Introduction¶

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.

## Example¶

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)
```