cimod.model.binary_quadratic_model

Module Contents

Functions

BinaryQuadraticModel(linear, quadratic, *args, **kwargs)
bqm_from_ising(linear, quadratic[, offset])
bqm_from_numpy_matrix(mat[, variables, offset, vartype])
bqm_from_qubo(Q[, offset])
extract_offset_and_vartype(*args, **kwargs)
get_cxxcimod_class(linear, quadratic, sparse)
make_BinaryQuadraticModel(linear, quadratic, sparse)	BinaryQuadraticModel factory.
make_BinaryQuadraticModel_from_JSON(obj)

cimod.model.binary_quadratic_model.BinaryQuadraticModel(linear, quadratic, *args, **kwargs)

cimod.model.binary_quadratic_model.bqm_from_ising(linear, quadratic, offset=0.0, **kwargs)

cimod.model.binary_quadratic_model.bqm_from_numpy_matrix(mat, variables: list = None, offset=0.0, vartype='BINARY', **kwargs)

Parameters:

variables (list) –

cimod.model.binary_quadratic_model.bqm_from_qubo(Q, offset=0.0, **kwargs)

cimod.model.binary_quadratic_model.extract_offset_and_vartype(*args, **kwargs)

cimod.model.binary_quadratic_model.get_cxxcimod_class(linear, quadratic, sparse)

cimod.model.binary_quadratic_model.make_BinaryQuadraticModel(linear, quadratic, sparse)

BinaryQuadraticModel factory.

Generate BinaryQuadraticModel class with the base class specified by the arguments linear and quadratic

Parameters:

• linear (dict) – linear bias

• quadratic (dict) – quadratic bias

• sparse (bool) – if true, the inner data will be a sparse matrix, otherwise the data will be a dense matrix

Returns:

generated BinaryQuadraticModel class

cimod.model.binary_quadratic_model.make_BinaryQuadraticModel_from_JSON(obj)