This paper presents important parts of a novel approach to molecular design that leverages quantum technologies to overcome the limitations of conventional chemistry. Drawing an analogy to genetic engineering, traditional chemical synthesis is replaced by a quantum-driven framework that utilizes “functional imprints”—abstract representations of properties grouped into four domains: power (P), service/presence (Pr), knowledge (K), and harmony (H). The methodology is demonstrated through the construction of a quadratic unconstrained binary optimization (QUBO) model designed to assemble a hypothetical molecule from four functional atoms each embodying energetic imprints from distinct sets (P, Pr, K, H). The QUBO formulation, implemented in Python, integrates objectives and constraints that reward optimal assignments while enforcing property distribution, assignment exclusivity, and diversity. Experimental results from D-Wave’s quantum processing unit and D-Wave’s simulated annealing are presented. Although both approaches yielded optimized results, simulated annealing achieved a higher aggregate molecule property value, indicative of subtle trade-offs between penalty minimization and raw objective performance. The study underscores the potential of quantum-based optimization methods to drive breakthroughs in molecular synthesis, paving the way for the creation of revolutionary materials.