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.

翻译：量子计算网络能够实现多个经典与量子计算节点间的可扩展协作及安全信息交换，同时执行大规模生成式AI计算任务和先进量子算法。量子计算网络克服了量子比特数量与纠缠对相干时间等限制，通过分布式处理增强噪声抑制能力，并借助多设备互联提升可扩展性，为生成式AI基础设施提供优势。然而，由于量子比特波动性和网络复杂性等因素，量子计算网络中的高效资源分配仍是一项关键挑战。本文提出一种面向量子计算网络的智能资源分配框架，以最小化资源成本提升网络可扩展性。为实现量子计算网络的可扩展性，我们将资源分配问题建模为随机规划，考虑量子比特与纠缠对的不确定保真度。进一步地，我们引入从生成式学习到量子机器学习的最新强化学习算法，以高效求解所提出的随机资源分配问题，实现最优量子资源分配。最后，我们针对支持量子生成式学习应用的异构量子计算网络优化资源分配，并提出一种基于多智能体强化学习的算法，无需先验知识即可学习最优资源分配策略。