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arXiv AI By An N. H. Phan, Dang Van Huynh, Muhammad Usman, Hoa T. Nguyen

Quantum Reinforcement Learning for Cost and Delay Tradeoffs in Quantum Cloud Orchestration

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Quantum Reinforcement Learning for Cost and Delay Tradeoffs in Quantum Cloud Orchestration proposes QRLQ, a scheduling framework that integrates parameterised quantum circuits with a dueling double deep Q‑network to balance execution cost and delay in quantum‑as‑a‑service environments. Simulation results show QRLQ outperforms heuristic baselines, achieving 5‑11% lower mean cost and up to 82% lower mean delay while maintaining fidelity within 2% of a fidelity‑greedy policy. Compared to a classical deep reinforcement learning baseline, QRLQ delivers comparable performance with 72% fewer trainable parameters.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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