The D-Wave quantum hardware was designed

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To logically represent the MCI problem we associate boolean variables xj to vertices j such that j (does not) belong to set A if xj = 1 (0) for j=1,...,n. Thus, for each possible combination of values ​​for x1,...,xn we associate a set A consisting of vertices j for which xj=1 with j=1,...,n. The cardinality of set A coincides with the sum x1 +...+ xn and A is independent if for every edge {i,j} in E, the product xi*xj = 0. Thus, the MCI problem can be viewed as the minimization of an energy function E(x1,...,xn) as shown in Figure 2.

To minimize functions of the form E(x1,...,xn) with linear and pairwise terms. The quantum representation of the function E(x1,...,xn) consists of matching the south africa email list pairwise terms to one or more pairs of qubits in the Chimera topology. Figure 2 shows an embedding of the problem in the Chimera topology.

To execute the CT algorithm, the evolution time ta and the number of iterations that the process will be repeated are established. For each iteration, the result is an energy value and a configuration of the qubits involved in the representation of the problem. The solutions of a CT algorithm are interpreted depending on the objective, either to obtain the minimum energy of the function E(x1,...,xn) or to find a configuration of the qubits such that they satisfy a specific property. For example, for the example in Figure 2, the minimum energy is 4 and the number of times it appears is 781 out of 1000 iterations. Therefore, the probability of success of the algorithm is p=781/1000.