ABSTRACT Quantum computing has entered the noisy intermediate‐scale quantum (NISQ) era, where limited qubit numbers, short coherence times, and high error rates pose significant challenges to reliable large‐scale execution. Efficient scheduling and resource allocation across heterogeneous quantum hardware are therefore crucial for maximizing system throughput, fidelity, and fairness. In this work, we propose a hardware‐aware reinforcement learning framework for quantum task scheduling in distributed NISQ systems. Our design explicitly models qubit‐level variability, including connectivity degree, coherence times, error rates, and throughput, while integrating task‐level constraints such as deadlines, priorities, and concurrency requirements. We formulate the scheduling problem as a constrained optimization task and instantiate it as a Markov decision process (MDP), enabling reinforcement learning agents to learn adaptive strategies. Specifically, we implement deep Q‐network (DQN) and proximal policy optimization (PPO) agents, and compare them against heuristic and random baselines. Experimental results demonstrate that PPO consistently outperforms DQN and heuristic methods, achieving higher task completion rates, fewer deadline violations, and more robust adaptation across different reward configurations. This work bridges quantum hardware modeling with reinforcement learning‐based scheduling, providing a practical pathway for resource optimization in distributed quantum computing environments.
Guo et al. (2026) studied this question.