This chapter explores D-Wave’s quantum computer, to address the social welfare optimization problem in electricity markets. The study considers key participants, including producers and consumers, and integrates sustainability by adding pollutant quotas from political tariffs as additional constraints. A dual-formulation approach is proposed: one as a mixed-integer linear program (MILP) and the other as a constrained quadratic model (CQM). The MILP aggregates electricity transactions from a market-wide perspective, while the CQM captures pairwise interactions to analyze social welfare distribution. Both formulations are solved using classical and quantum methods, with a comparative analysis highlighting the efficiency of quantum annealing in addressing complex problems. The impact of the dataset’s characteristics on solution times is also evaluated, offering insights into quantum annealing’s scalability in the energy sector. The results show that integrating pollutant quotas into the optimization framework achieves balanced market outcomes, supporting sustainability while optimizing social welfare. The findings underscore the potential of quantum annealing in enhancing decision-making in electricity markets, promoting efficient resource allocation, and regulatory compliance.