Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing

TL;DR

This paper presents a stochastic optimization model for resource management in quantum cloud computing, jointly optimizing entangled pairs, routing, and qubit resources to minimize costs amid demand uncertainties.

Contribution

It introduces a two-stage stochastic programming model with Benders decomposition for efficient quantum resource provisioning in cloud environments, addressing entanglement and demand fluctuations.

Findings

Achieves up to 49.43% cost reduction compared to baseline.

Effectively manages entanglement routing and resource allocation under uncertainty.

Demonstrates the model's ability to minimize total costs in quantum cloud scenarios.

Abstract

Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources, such as quantum computers, to perform resource-intensive tasks. Like traditional cloud computing platforms, QCC providers can offer both reservation and on-demand plans for quantum resource provisioning to satisfy users' requirements. However, the fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. Furthermore, in distributed QCC, entanglement routing is a critical component of quantum networks that enables remote entanglement communication between users and QCC providers. Further, maintaining entanglement fidelity in quantum networks is challenging due to the requirement for high-quality entanglement routing, especially when accessing the providers over long distances. To address these challenges, we propose a resource allocation model to provision quantum computing and networking resources. In particular, entangled pairs, entanglement routing, qubit resources, and circuits' waiting time are jointly optimized to achieve minimum total costs. We formulate the proposed model based on the two-stage stochastic programming, which takes into account the uncertainties of fidelity and qubit requirements, and quantum circuits' waiting time. Furthermore, we apply the Benders decomposition algorithm to divide the proposed model into sub-models to be solved simultaneously. Experimental results demonstrate that our model can achieve the optimal total costs and reduce total costs at most 49.43\% in comparison to the baseline model.