dwave.embedding.chain_breaks.MinimizeEnergy¶
- class MinimizeEnergy(bqm, embedding)[source]¶
Unembed samples by minimizing local energy for broken chains.
- Parameters
bqm (
BinaryQuadraticModel
) – Binary quadratic model associated with the source graph.embedding (dict) – Mapping from source graph to target graph as a dict of form {s: [t, …], …}, where s is a source-model variable and t is a target-model variable.
Examples
This example embeds from a triangular graph to a square graph, chaining target-nodes 2 and 3 to represent source-node c, and unembeds minimizing the energy for the samples. The first two sample have unbroken chains, the second two have broken chains.
>>> import dimod >>> import numpy as np ... >>> h = {'a': 0, 'b': 0, 'c': 0} >>> J = {('a', 'b'): 1, ('b', 'c'): 1, ('a', 'c'): 1} >>> bqm = dimod.BinaryQuadraticModel.from_ising(h, J) >>> embedding = {'a': [0], 'b': [1], 'c': [2, 3]} >>> cbm = dwave.embedding.MinimizeEnergy(bqm, embedding) >>> samples = np.array([[+1, -1, +1, +1], ... [-1, -1, -1, -1], ... [-1, -1, +1, -1], ... [+1, +1, -1, +1]], dtype=np.int8) >>> chains = [embedding['a'], embedding['b'], embedding['c']] >>> unembedded, idx = cbm(samples, chains) >>> unembedded array([[ 1, -1, 1], [-1, -1, -1], [-1, -1, 1], [ 1, 1, -1]], dtype=int8) >>> idx array([0, 1, 2, 3])
Methods
__init__
(bqm, embedding)