A System Aware Resource Allocation for Distributed Workflows in Quantum Computing Environments

2026-05-18

AI Summary

This work addresses the challenge of simultaneously optimizing fidelity, execution time, and communication overhead in large-scale distributed quantum workflows on current quantum computing platforms. Targeting hybrid classical-quantum network environments, it proposes the first resource allocation framework that integrates system-level multidimensional cost metrics. The approach models workflows as graphs and introduces enhanced graph partitioning and scheduling algorithms to efficiently map quantum tasks onto NISQ devices. Experimental results demonstrate that, compared to existing methods, the proposed framework achieves average improvements of approximately 5% in execution time, 30% in communication overhead, 40% in waiting time, and 2% in fidelity, substantially enhancing overall resource allocation efficiency.

Abstract

Rapid advancements in cloud based platforms providing access to quantum computing capabilities have opened up several challenges for efficient usage of these highly delicate and costly devices. Although most of the current systems use a priority based access protocol, they are unable to fully support reliable, efficient, and scalable execution of larger-scale applications. To overcome this limitation, we propose a comprehensive solution for efficient allocation of quantum programs to appropriate quantum devices, considering all the relevant cost metrics into account including, fidelity, execution time and communication overhead. We also formulate use-cases for distributed quantum workflow and propose modified graph based algorithms to solve for allocation of such use-cases, assuming a hybrid classical-quantum network. Since hardware advancements in large standalone devices is an ongoing process, it is critical to investigate such distributed workflows to maximize the best utilization of current NISQ devices. Our empirical study shows that the proposed techniques perform better than state-of-the-art methods for almost all evaluation parameters, with average improvements of approximately $5\%$ in execution time, $30\%$ in communication overhead, $40\%$ in wait time and $2\%$ in fidelity, providing better solutions to efficient allocation strategies.

Problem

• quantum computing
• resource allocation
• distributed workflows
• NISQ devices
• hybrid classical-quantum network

Innovation

• quantum resource allocation
• distributed quantum workflows
• system-aware scheduling
• hybrid classical-quantum networks
• NISQ devices