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Recently, the use of quantum cloud computing for different applications has been increasing. For instance, weather forecasting, financial modeling, healthcare, and automation are geographically distributed in practice. These applications are workflows and consist of compute-intensive dependent tasks with precedence constraints. However, workflow processing on quantum-based cloud services still faces issues in the literature regarding makespan and energy consumption. This study presents the Multi-objective Quantum-inspired Genetic Algorithm (MQGA) to address the problems of workflow scheduling in the hybrid cloud, attempting to reduce makespan and energy consumption simultaneously. The proposed algorithm relies on the concept and principle of quantum mechanics, which explores the computational power of quantum computing. It adopted a qubit to represent the individual chromosome for better …