<p>The Salp Swarm Algorithm (SSA) is an established metaheuristic valued for simplicity and effectiveness but struggles with reduced diversity, slow convergence, and susceptibility to local optima, especially in high-dimensional and multimodal problems. This study introduces the Quantum-Classical Harmonic Oscillator Salp Swarm Algorithm (QHO-SSA), a novel hybrid quantum-classical optimization framework designed to overcome these limitations. QHO-SSA leverages quantum computing techniques - quantum state initialization and quantum harmonic oscillator-inspired local search - to significantly enhance exploration diversity and accelerate convergence. The algorithm is rigorously evaluated against twenty-three challenging IEEE CEC 2017 benchmark functions at 10 and 30 dimensions, and benchmarked against standard SSA and advanced methods such as Harris Hawks Optimization (HHO), Differential Evolution (DE), Particle Swarm Optimization (PSO), and Quantum-Behaved Particle Swarm Optimization (QPSO). Experimental results on IEEE CEC 2017 benchmark functions demonstrate that QHO-SSA achieves 32–48% improved solution quality compared to standard SSA across 10D and 30D problems, with convergence rates accelerated by approximately 40% in high-dimensional scenarios. Specifically, on the multimodal F1 function (10D), QHO-SSA achieves a fitness value of 2.73E + 02 compared to SSA’s 4.02E + 03, representing a 93.2% improvement. Similarly, on the hybrid F10 function (30D), QHO-SSA records 3.85E + 03 versus SSA’s 5.50E + 03, demonstrating consistent superiority. Statistical validation through Friedman tests (p-values &lt; 0.001) confirms significant performance differences, establishing QHO-SSA’s robustness and scalability for complex optimization tasks. Furthermore, this study highlights the practical feasibility and potential advantages of hybrid quantum-classical optimization methods in addressing contemporary computational limitations. </p>