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CloudQC: A Network-aware Framework for Multi-tenant Distributed Quantum Computing

Ruilin Zhou, Yuhang Gan, Yi Liu, Chen Qian

TL;DR

CloudQC tackles scalable distributed quantum computing in a multi-tenant quantum cloud by jointly optimizing circuit placement and network resource scheduling. It introduces two core components: circuit placement (partitioning circuits, identifying feasible QPU sets via modularity-based community detection, and mapping partitions to QPUs) and network scheduling (priority-based allocation of EPR resources with redundancy for critical gates) that account for probabilistic EPR generation. Using a Python-based discrete-event simulator and real circuit workloads, CloudQC demonstrates substantial reductions in job completion time and improved resource utilization over single-circuit placement baselines. This work lays groundwork for practical quantum cloud infrastructures and highlights directions for hardware improvements in EPR generation and more sophisticated network-aware scheduling.

Abstract

Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.

CloudQC: A Network-aware Framework for Multi-tenant Distributed Quantum Computing

TL;DR

CloudQC tackles scalable distributed quantum computing in a multi-tenant quantum cloud by jointly optimizing circuit placement and network resource scheduling. It introduces two core components: circuit placement (partitioning circuits, identifying feasible QPU sets via modularity-based community detection, and mapping partitions to QPUs) and network scheduling (priority-based allocation of EPR resources with redundancy for critical gates) that account for probabilistic EPR generation. Using a Python-based discrete-event simulator and real circuit workloads, CloudQC demonstrates substantial reductions in job completion time and improved resource utilization over single-circuit placement baselines. This work lays groundwork for practical quantum cloud infrastructures and highlights directions for hardware improvements in EPR generation and more sophisticated network-aware scheduling.

Abstract

Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.
Paper Structure (25 sections, 5 equations, 22 figures, 3 tables, 3 algorithms)

This paper contains 25 sections, 5 equations, 22 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Quantum Cloud Model
  4. Problem Formulation
  5. Design Objectives
  6. The Multi-tenant Circuit Placement Problem
  7. Network Scheduling
  8. Problem Formulation
  9. CloudQC's Design
  10. Design Overview
  11. Circuit Placement
  12. Network Scheduling
  13. Evaluation
  14. Evaluation Setting
  15. Evaluation on Circuit Placement
  16. ...and 10 more sections

Figures (22)

  • Figure 1: Quantum circuit of a 4-qubit VQE algorithm
  • Figure 2: Quantum Cloud
  • Figure 3: (a) An example circuit spams three QPUs. (b)Corresponding remote DAG that only contains inter-QPU remote gates
  • Figure 4: Overview of our scheduler workflow
  • Figure 5: Example of a quantum cloud with three DQC jobs.
  • ...and 17 more figures