Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation

TL;DR

This paper presents an intelligent resource allocation framework for quantum computing networks, leveraging reinforcement learning to optimize resource use amid network uncertainties, enhancing scalability and efficiency for generative AI tasks.

Contribution

It introduces a stochastic programming model combined with RL algorithms for optimal resource allocation in quantum networks, including a multi-agent RL approach for heterogeneous systems.

Findings

Reinforcement learning effectively optimizes quantum resource allocation.

The proposed framework improves network scalability and reduces resource costs.

Multi-agent RL adapts to heterogeneous quantum network conditions.

Abstract

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.