QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

Quantum cloud computing enables remote access to quantum processors, yet the heterogeneity and noise of available quantum hardware create significant challenges for efficient resource orchestration. These issues complicate the optimisation of quantum task allocation and scheduling, as existing heuristic methods fall short in adapting to dynamic conditions or effectively balancing execution fidelity and time. Here, we propose QFOR, a Q uantum F idelity-aware O rchestration of tasks across heterogeneous quantum nodes in cloud-based environments using Deep R einforcement learning. We model the quantum task orchestration as a Markov Decision Process and employ the Proximal Policy Optimisation algorithm to learn adaptive scheduling policies, using IBM quantum processor calibration data for noise-aware performance estimation. Our configurable framework balances overall quantum task execution fidelity and time, enabling adaptation to different operational priorities. Extensive evaluation demonstrates that QFOR is adaptive and achieves significant performance with 29.5-84% improvements in relative fidelity performance over other deep reinforcement learning and heuristic baselines. Furthermore, it maintains comparable quantum execution times, contributing to cost-efficient use of quantum computation resources.