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Quantum computing networks enable scalable collaboration and secureinformation exchange among multiple classical and quantum computing nodes whileexecuting large-scale generative AI computation tasks and advanced quantumalgorithms. Quantum computing networks overcome limitations such as the numberof qubits and coherence time of entangled pairs and offer advantages forgenerative AI infrastructure, including enhanced noise reduction throughdistributed processing and improved scalability by connecting multiple quantumdevices. However, efficient resource allocation in quantum computing networksis a critical challenge due to factors including qubit variability and networkcomplexity. In this article, we propose an intelligent resource allocationframework for quantum computing networks to improve network scalability withminimized resource costs. To achieve scalability in quantum computing networks,we formulate the resource allocation problem as stochastic programming,accounting for the uncertain fidelities of qubits and entangled pairs.Furthermore, we introduce state-of-the-art reinforcement learning (RL)algorithms, from generative learning to quantum machine learning for optimalquantum resource allocation to resolve the proposed stochastic resourceallocation problem efficiently. Finally, we optimize the resource allocation inheterogeneous quantum computing networks supporting quantum generative learningapplications and propose a multi-agent RL-based algorithm to learn the optimalresource allocation policies without prior knowledge.

Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation