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This paper focuses on the Traveling Repairman Problem (TRP), an extension of the Traveling Salesman Problem (TSP), which has gained significant attention in various practical fields such as humanitarian logistics, machine scheduling, computer networking, and service-based distribution. Previous research on the TRP has primarily focused on exact and approximate algorithms, with limited attention given to heuristics. This paper introduces a new hybrid method called Guided Quantum Particle Swarm Optimization (GQPSO) to effectively solve the TRP. This GQPSO apporoach combines the Quantum Particle Swarm Optimization (QPSO) metaheuristic with the Guided Local Search (GLS) algorithm. We introduced several enhancements to improve the efficiency of QPSO when addressing the TRP: (i) The initialization phase of the QPSO algorithm is modified. (ii) A guided local search method is integrated within QPSO and used as an intensification strategy for each position generated throughout the search process. (iii) Several acceleration strategies are implemented to expedite the process of the local search procedure. The experimental results achieved on a collection of benchmark problems sourced from the literature clearly prove the effectiveness of the proposed hybrid approach for addressing the TRP when compared to other heuristic methods from the state-of-the-art.