The Job-Shop Scheduling Problem (JSSP) stands as one of the most renowned challenges in scheduling. It is characterized as a disjunctive problem, wherein a solution is fully depicted through an oriented disjunctive graph, with earliest starting times computed using a longest path algorithm. The complexity of solving this problem arises in part from the requirement that disjunctive graphs representing solutions must be acyclic. Consequently, enumerating these graphs is feasible for small-scale instances only. A significant advancement in this field, credited to (Bierwith, 1995), is the introduction of the 'vector by repetition' (commonly known as Bierwith's vector). Notably, this vector possesses the property that it can be mapped to an acyclic disjunctive graph, thereby enabling the mapping of a vector to a solution. This property has facilitated the development of highly efficient resolution schemes, as it allows the enumeration of solutions only i.e. acyclic disjunctive graphs. Our objective is to demonstrate how Bierwith's vector can be integrated into a Quantum Approximate Optimization Algorithm (QAOA) to tackle the job-shop problem using a novel quantum approach.