Working With Different Topologies¶

The examples shows how to construct software samplers with the same structure as the QPU, and how to work with embeddings with different topologies.

The code examples below will use the following imports:

>>> import neal
>>> import dimod
>>> import dwave_networkx as dnx
>>> import networkx as nx
>>> import dwave.embedding
...
>>> from dwave.system import DWaveSampler, EmbeddingComposite


Creating a Chimera Sampler¶

As detailed in Using a Classical Solver, you might want to use a classical solver while developing your code or writing tests. However, it is sometimes useful to work with a software solver that behaves more like a quantum computer.

One of the key features of the quantum computer is its working graph, which defines the connectivity allowed by the binary quadratic model.

To create a software solver with the same connectivity as a D-Wave 2000Q quantum computer we first need a representation of the Chimera graph which can be obtained from the dwave_networkx project using the chimera_graph() function.

>>> C16 = dnx.chimera_graph(16)


Next, we need a software sampler. We will use the neal.SimulatedAnnealingSampler found in dwave_neal, though the tabu.TabuSampler from dwave-tabu would work equally well.

>>> classical_sampler = neal.SimulatedAnnealingSampler()


Now, with a classical sampler and the desired graph, we can use dimod’s dimod.StructuredComposite to create a Chimera-structured sampler.

>>> sampler = dimod.StructureComposite(classical_sampler, C16.nodes, C16.edges)


This sampler accepts Chimera-structured problems. In this case we create an Ising problem.

>>> h = {v: 0.0 for v in C16.nodes}
>>> J = {(u, v): 1 for u, v in C16.edges}
>>> sampleset = sampler.sample_ising(h, J)


We can even use the sampler with the dwave.system.EmbeddingComposite

>>> embedding_sampler = EmbeddingComposite(sampler)


Finally, we can confirm that our sampler matches the dwave.system.DWaveSampler’s structure. We make sure that our QPU has the same topology we have been simulating. Also note that the working graph of the QPU is usually a subgraph of the full hardware graph.

>>> qpu_sampler = DWaveSampler(solver={'qpu': True, 'num_active_qubits__within': [2000, 2048]})
>>> QPUGraph = nx.Graph(qpu_sampler.edgelist)
>>> all(v in C16.nodes for v in QPUGraph.nodes)
True
>>> all(edge in C16.edges for edge in QPUGraph.edges)
True


Creating a Pegasus Sampler¶

Another topology of interest is the Pegasus topology.

As above, we can use the generator function dwave_networkx.pegasus_graph() found in dwave_networkx and the neal.SimulatedAnnealingSampler found in dwave_neal to construct a sampler.

>>> P6 = dnx.pegasus_graph(6)
>>> classical_sampler = neal.SimulatedAnnealingSampler()
>>> sampler = dimod.StructureComposite(classical_sampler, P6.nodes, P6.edges)


Working With Embeddings¶

The example above using the EmbeddingComposite hints that we might be interested in trying embedding with different topologies.

One thing we might be interested in is the chain length when embedding our problem. Say that we have a fully connected problem with 40 variables and we want to know the chain length needed to embed it on a 2048 node Chimera graph.

We can use dwave-system’s find_clique_embedding() function to find the embedding and determine the maximum chain length.

>>> num_variables = 40
>>> embedding = dwave.embedding.chimera.find_clique_embedding(num_variables, 16)
>>> max(len(chain) for chain in embedding.values())
11


Similarly we can explore clique embeddings for a 40-variables fully connected problem with a 680 node Pegasus graph using dwave-system’s find_clique_embedding() function

>>> num_variables = 40
>>> embedding = dwave.embedding.pegasus.find_clique_embedding(num_variables, 6)
>>> max(len(chain) for chain in embedding.values())
6