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

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressing the tradeoff between execution costs and system performance. While heuristic methods rely on predefined scheduling rules, classical deep reinforcement learning (DRL) models may require more trainable parameters in this setting. Motivated by the potential of parameterised quantum circuits (PQCs) a

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