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arXiv · 2607.25358

QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization

Tam N. Pham·Hoa T. Nguyen·Quan Le-Trung
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Abstract

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.

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BibTeXRIS

Tam N. Pham, Hoa T. Nguyen, Quan Le-Trung. 2026-07-28. QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization. https://doi.org/10.1109/cloud72782.2026.00059

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