dimod.utilities.qubo_energy¶

qubo_energy
(sample, Q, offset=0.0)[source]¶ Calculate the energy for the specified sample of a QUBO model.
Energy of a sample for a binary quadratic model is defined as a sum, offset by the constant energy offset associated with the model, of the sample multipled by the linear bias of the variable and all its interactions. For a quadratic unconstrained binary optimization (QUBO) model,
\[E(\mathbf{x}) = \sum_{u,v} Q_{u,v} x_u x_v + c\]where \(x_v\) is the sample, \(Q_{u,v}\) a matrix of biases, and \(c\) the energy offset.
Parameters:  sample (dict[variable, spin]) – Sample for a binary quadratic model as a dict of form {v: bin, …}, where keys are variables of the model and values are binary (either 0 or 1).
 Q (dict[(variable, variable), coefficient]) – QUBO coefficients in a dict of form {(u, v): coefficient, …}, where keys are 2tuples of variables of the model and values are biases associated with the pair of variables. Tuples (u, v) represent interactions and (v, v) linear biases.
 offset (numeric, optional, default=0) – Constant offset to be applied to the energy. Default 0.
Returns: The induced energy.
Return type: Notes
No input checking is performed.
Examples
This example calculates the energy of a sample representing two zeros for a QUBO model of two variables that have positive biases of value 1 and are positively coupled with an interaction of value 1.
>>> import dimod >>> sample = {1: 0, 2: 0} >>> Q = {(1, 1): 1, (2, 2): 1, (1, 2): 1} >>> dimod.qubo_energy(sample, Q, 0.5) 0.5
References