  We propose an innovative Parallel Quantum Local Search (PQLS) methodology
that leverages the capabilities of small-scale quantum computers to efficiently
address complex combinatorial optimization problems. Traditional Quantum Local
Search (QLS) methods face limitations due to the sequential nature of solving
sub-problems, which arises from dependencies between their solutions. Our
approach transcends this constraint by simultaneously executing multiple QLS
pathways and aggregating their most effective outcomes at certain intervals to
establish a ``generation''. Each subsequent generation commences with the
optimal solution from its predecessor, thereby significantly accelerating the
convergence towards an optimal solution. Our findings demonstrate the profound
impact of parallel quantum computing in enhancing the resolution of Ising
problems, which are synonymous with combinatorial optimization challenges.
