def _best_subset(self, n_qubits):
if n_qubits == <NUM_LIT:1>:<EOL><INDENT>return np.array([<NUM_LIT:0>])<EOL><DEDENT>device_qubits = self.coupling_map.size()<EOL>cmap = np.asarray(self.coupling_map.get_edges())<EOL>data = np.ones_like(cmap[:, <NUM_LIT:0>])<EOL>sp_cmap = sp.coo_matrix((data, (cmap[:, <NUM_LIT:0>], cmap[:, <NUM_LIT:1>])),<EOL>shape=(device_qubits, device_qubits)).tocsr()<EOL>best = <NUM_LIT:0><EOL>best_map = None<EOL>for k in range(sp_cmap.shape[<NUM_LIT:0>]):<EOL><INDENT>bfs = cs.breadth_first_order(sp_cmap, i_start=k, directed=False,<EOL>return_predecessors=False)<EOL>connection_count = <NUM_LIT:0><EOL>sub_graph = []<EOL>for i in range(n_qubits):<EOL><INDENT>node_idx = bfs[i]<EOL>for j in range(sp_cmap.indptr[node_idx],<EOL>sp_cmap.indptr[node_idx + <NUM_LIT:1>]):<EOL><INDENT>node = sp_cmap.indices[j]<EOL>for counter in range(n_qubits):<EOL><INDENT>if node == bfs[counter]:<EOL><INDENT>connection_count += <NUM_LIT:1><EOL>sub_graph.append([node_idx, node])<EOL>break<EOL><DEDENT><DEDENT><DEDENT><DEDENT>if connection_count > best:<EOL><INDENT>best = connection_count<EOL>best_map = bfs[<NUM_LIT:0>:n_qubits]<EOL>mapping = {}<EOL>for edge in range(best_map.shape[<NUM_LIT:0>]):<EOL><INDENT>mapping[best_map[edge]] = edge<EOL><DEDENT>new_cmap = [[mapping[c[<NUM_LIT:0>]], mapping[c[<NUM_LIT:1>]]] for c in sub_graph]<EOL>rows = [edge[<NUM_LIT:0>] for edge in new_cmap]<EOL>cols = [edge[<NUM_LIT:1>] for edge in new_cmap]<EOL>data = [<NUM_LIT:1>]*len(rows)<EOL>sp_sub_graph = sp.coo_matrix((data, (rows, cols)),<EOL>shape=(n_qubits, n_qubits)).tocsr()<EOL>perm = cs.reverse_cuthill_mckee(sp_sub_graph)<EOL>best_map = best_map[perm]<EOL><DEDENT><DEDENT>return best_map<EOL>
Computes the qubit mapping with the best connectivity.

        Args:
            n_qubits (int): Number of subset qubits to consider.

        Returns:
            ndarray: Array of qubits to use for best connectivity mapping.