# Introduction¶

D-Wave NetworkX provides tools for working with Quantum Processing Unit (QPU)
topology graphs, such as the Pegasus used on the Advantage^{TM} 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.

For an introduction to quantum processing unit (QPU) topologies such as the Pegasus graph, see Topology.

For an introduction to binary quadratic models (BQMs), see Binary Quadratic Models.

For an introduction to samplers, see Samplers and Composites.

## Example¶

This example creates a Pegasus graph (used by Advantage) and a small Zephyr graph
(used by the Advantage2^{TM} prototype made available in Leap^{TM} in June 2022):

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