dwave.embedding.embed_ising¶

embed_ising
(source_h, source_J, embedding, target_adjacency, chain_strength=1.0)[source]¶ Embed an Ising problem onto a target graph.
Parameters:  source_h (dict[variable, bias]/list[bias]) – Linear biases of the Ising problem. If a list, the list’s indices are used as variable labels.
 source_J (dict[(variable, variable), bias]) – Quadratic biases of the Ising problem.
 embedding (dict) – Mapping from source graph to target graph as a dict of form {s: {t, …}, …}, where s is a sourcemodel variable and t is a targetmodel variable.
 target_adjacency (dict/
networkx.Graph
) – Adjacency of the target graph as a dict of form {t: Nt, …}, where t is a targetgraph variable and Nt is its set of neighbours.  chain_strength (float, optional) – Magnitude of the quadratic bias (in SPINspace) applied between variables to form a chain, with the energy penalty of chain breaks set to 2 * chain_strength.
Returns: A 2tuple:
dict[variable, bias]: Linear biases of the target Ising problem.
dict[(variable, variable), bias]: Quadratic biases of the target Ising problem.
Return type: Examples
This example embeds a triangular Ising problem representing a \(K_3\) clique into a square target graph by mapping variable c in the source to nodes 2 and 3 in the target.
>>> import networkx as nx ... >>> target = nx.cycle_graph(4) >>> # Ising problem biases >>> h = {'a': 0, 'b': 0, 'c': 0} >>> J = {('a', 'b'): 1, ('b', 'c'): 1, ('a', 'c'): 1} >>> # Variable c is a chain >>> embedding = {'a': {0}, 'b': {1}, 'c': {2, 3}} >>> # Embed and show the resulting biases >>> th, tJ = dwave.embedding.embed_ising(h, J, embedding, target) >>> th # doctest: +SKIP {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0} >>> tJ # doctest: +SKIP {(0, 1): 1.0, (0, 3): 1.0, (1, 2): 1.0, (2, 3): 1.0}
See also