  Quantum computing networks enable scalable collaboration and secure
information exchange among multiple classical and quantum computing nodes while
executing large-scale generative AI computation tasks and advanced quantum
algorithms. Quantum computing networks overcome limitations such as the number
of qubits and coherence time of entangled pairs and offer advantages for
generative AI infrastructure, including enhanced noise reduction through
distributed processing and improved scalability by connecting multiple quantum
devices. However, efficient resource allocation in quantum computing networks
is a critical challenge due to factors including qubit variability and network
complexity. In this article, we propose an intelligent resource allocation
framework for quantum computing networks to improve network scalability with
minimized 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 optimal
quantum resource allocation to resolve the proposed stochastic resource
allocation problem efficiently. Finally, we optimize the resource allocation in
heterogeneous quantum computing networks supporting quantum generative learning
applications and propose a multi-agent RL-based algorithm to learn the optimal
resource allocation policies without prior knowledge.
