  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.
