Optimal Stochastic Resource Allocation for Distributed Quantum Computing

TL;DR

This paper introduces a stochastic programming-based resource allocation scheme for distributed quantum computing, effectively managing quantum resource uncertainties to minimize deployment costs.

Contribution

It presents a novel two-stage stochastic programming model tailored for quantum resource allocation in DQC, addressing uncertainties in demand, fidelity, and quantum channel noise.

Findings

The scheme reduces overall quantum resource deployment costs.

It balances utilization of fixed and on-demand quantum computers.

The approach effectively manages quantum network uncertainties.

Abstract

With the advent of interconnected quantum computers, i.e., distributed quantum computing (DQC), multiple quantum computers can now collaborate via quantum networks to perform massively complex computational tasks. However, DQC faces problems sharing quantum information because it cannot be cloned or duplicated between quantum computers. Thanks to advanced quantum mechanics, quantum computers can teleport quantum information across quantum networks. However, challenges to utilizing efficiently quantum resources, e.g., quantum computers and quantum channels, arise in DQC due to their capabilities and properties, such as uncertain qubit fidelity and quantum channel noise. In this paper, we propose a resource allocation scheme for DQC based on stochastic programming to minimize the total deployment cost for quantum resources. Essentially, the two-stage stochastic programming model is formulated to handle the uncertainty of quantum computing demands, computing power, and fidelity in quantum networks. The performance evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers while minimizing the overall cost of provisioning under uncertainty.