Problem
Research questions and friction points this paper is trying to address.
Quantum Cloud Orchestration
Noise-aware Scheduling
Load Imbalance
Task Rescheduling
Execution Fidelity
QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization
🤖 AI Summary
This work addresses the limitations of existing quantum cloud orchestrators, which neglect backend noise characteristics, leading to low fidelity, unbalanced workloads, and frequent rescheduling. To overcome these issues, we propose QCOEM, a novel framework that uniquely integrates evolutionary multi-objective optimization with preference-driven augmented achievement scalarizing function (AASF) scalarization. QCOEM jointly optimizes task makespan, execution error rate, and load balancing across heterogeneous, time-varying quantum backends, and efficiently selects optimal scheduling solutions from non-convex Pareto fronts. Experimental results demonstrate that QCOEM eliminates rescheduling entirely, improves average execution fidelity by approximately 30%, and maintains lightweight scheduling overhead.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum Cloud Orchestration
Evolutionary Multi-Objective Optimization
NSGA-II/NSGA-III
Noise-Aware Scheduling
Pareto Front Selection
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