Novel scheduler for distributed quantum computing that compares execution plans at runtime. Shows reconstruction dominates overhead (83%), distributed execution reduces P90 latency 39%, and scheduler overhead remains negligible (<0.038%).

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Researchers created a Graph Neural Network framework that predicts quantum circuit performance on different processors before compilation, reducing computational costs by up to 10x and enabling optimal task scheduling across diverse quantum hardware.

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GNN-based framework predicts quantum circuit fidelity across multiple QPUs before compilation, enabling efficient scheduling that balances execution quality with parallelism while reducing computational overhead.

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Greedy scheduling algorithm for multi-core quantum systems achieves 40% makespan reduction by exploiting fine-grained gate dependencies—key advancement for scalable quantum architectures.

#QuantumScheduling #MultiCoreQuantum #Research