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This paper introduces QCOEM, a Quantum Cloud Orchestration framework that employs Evolutionary Multi-Objective Optimization to enhance workload scheduling across diverse quantum computation backends. By utilizing NSGA-II and NSGA-III algorithms, the framework effectively minimizes mean completion time, execution error rates, and load imbalances, achieving zero task rescheduling and a 30% increase in mean fidelity compared to traditional noise-agnostic methods. The results underscore QCOEM's capability to provide stable, high-fidelity execution while ensuring efficient resource management in quantum cloud environments.
Zero task rescheduling and a 30% boost in execution fidelity could redefine quantum cloud workload management.
Quantum cloud platforms need to dynamically orchestrate workloads across heterogeneous quantum computation backends whose noise profiles, qubit topologies, and queues vary over time. Existing orchestrators use noise-agnostic heuristics that ignore backend-specific errors, causing reduced execution fidelity, load imbalance, and frequent rescheduling. To address these challenges, we propose QCOEM - a Quantum Cloud Orchestration framework that leverages Evolutionary algorithms for Multi-objective optimization of quantum task scheduling. We compare NSGA-II and NSGA-III for jointly minimizing mean completion time, execution error rate, and load imbalance. To select schedules from a non-convex Pareto front, we apply an Augmented Achievement Scalarization Function (AASF) as a preference-based decision rule that maps the Pareto set to a single dispatchable schedule aligned with user priorities. Our extensive performance evaluation in a heterogeneous quantum cloud environment shows zero task rescheduling and about 30% higher mean fidelity than noise-agnostic heuristics, while maintaining bounded scheduling overhead. The experiment results indicate that our QCOEM framework can deliver stable, high-fidelity execution and lightweight resource management for quantum cloud computing.
