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QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management

Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya

TL;DR

QSimPy introduces a Python-based discrete-event simulation framework for learning-centric quantum cloud resource management, bridging the gap between quantum cloud hardware modeling and reinforcement learning optimization. Built atop SimPy and integrated with Gymnasium, it provides modular components for quantum resources, tasks, and RL-driven decision making, facilitating rapid prototyping and evaluation of scheduling policies. The paper formalizes a QSTAR RL model, demonstrates a DRL-based quantum task scheduling example using OpenQASM/CSV workloads and IBM QNode data, and shows convergence of policy performance, highlighting the framework's potential to accelerate robust resource management in quantum clouds. By enabling seamless coupling with industry-standard RL libraries, QSimPy offers a practical platform for researchers and practitioners to design, benchmark, and extend learning-based quantum cloud orchestration strategies.

Abstract

Quantum cloud computing is an emerging computing paradigm that allows seamless access to quantum hardware as cloud-based services. However, effective use of quantum resources is challenging and necessitates robust simulation frameworks for effective resource management design and evaluation. To address this need, we proposed QSimPy, a novel discrete-event simulation framework designed with the main focus of facilitating learning-centric approaches for quantum resource management problems in cloud environments. Underpinned by extensibility, compatibility, and reusability principles, QSimPy provides a lightweight simulation environment based on SimPy, a well-known Python-based simulation engine for modeling dynamics of quantum cloud resources and task operations. We integrate the Gymnasium environment into our framework to support the creation of simulated environments for developing and evaluating reinforcement learning-based techniques for optimizing quantum cloud resource management. The QSimPy framework encapsulates the operational intricacies of quantum cloud environments, supporting research in dynamic task allocation and optimization through DRL approaches. We also demonstrate the use of QSimPy in developing reinforcement learning policies for quantum task placement problems, demonstrating its potential as a useful framework for future quantum cloud research.

QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management

TL;DR

QSimPy introduces a Python-based discrete-event simulation framework for learning-centric quantum cloud resource management, bridging the gap between quantum cloud hardware modeling and reinforcement learning optimization. Built atop SimPy and integrated with Gymnasium, it provides modular components for quantum resources, tasks, and RL-driven decision making, facilitating rapid prototyping and evaluation of scheduling policies. The paper formalizes a QSTAR RL model, demonstrates a DRL-based quantum task scheduling example using OpenQASM/CSV workloads and IBM QNode data, and shows convergence of policy performance, highlighting the framework's potential to accelerate robust resource management in quantum clouds. By enabling seamless coupling with industry-standard RL libraries, QSimPy offers a practical platform for researchers and practitioners to design, benchmark, and extend learning-based quantum cloud orchestration strategies.

Abstract

Quantum cloud computing is an emerging computing paradigm that allows seamless access to quantum hardware as cloud-based services. However, effective use of quantum resources is challenging and necessitates robust simulation frameworks for effective resource management design and evaluation. To address this need, we proposed QSimPy, a novel discrete-event simulation framework designed with the main focus of facilitating learning-centric approaches for quantum resource management problems in cloud environments. Underpinned by extensibility, compatibility, and reusability principles, QSimPy provides a lightweight simulation environment based on SimPy, a well-known Python-based simulation engine for modeling dynamics of quantum cloud resources and task operations. We integrate the Gymnasium environment into our framework to support the creation of simulated environments for developing and evaluating reinforcement learning-based techniques for optimizing quantum cloud resource management. The QSimPy framework encapsulates the operational intricacies of quantum cloud environments, supporting research in dynamic task allocation and optimization through DRL approaches. We also demonstrate the use of QSimPy in developing reinforcement learning policies for quantum task placement problems, demonstrating its potential as a useful framework for future quantum cloud research.
Paper Structure (21 sections, 2 equations, 6 figures, 2 tables, 1 algorithm)

This paper contains 21 sections, 2 equations, 6 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Main Contributions
  3. Paper Organization
  4. Related Work
  5. RL-based Problem Formulation and Model
  6. Problem Formulation
  7. Reinforcement Learning-based QSTAR Model
  8. The QSimPy Framework
  9. Design Overview
  10. Architecture Design and Functionality
  11. Core Simulation Engine
  12. Dataset Generation and Modeling
  13. Gymnasium-based Environment
  14. RL Framework Connector
  15. Simulation Workflow
  16. ...and 6 more sections

Figures (6)

  • Figure 1: A high-level view of quantum cloud computing environments and a sample procedure from user invocation to quantum task execution.
  • Figure 2: An overview of the QSimPy Framework.
  • Figure 3: Feature extraction and modeling for (a) quantum computation tasks (QTasks) and (b) quantum computation nodes (QNodes) in QSimPy
  • Figure 4: The sequence of main steps in the action decision-making process for each incoming QTask within the QSimPy simulation environment.
  • Figure 5: Episode rewards of different iterations showed that the DRL-based task scheduling policy has been trained in QSimPy environment
  • ...and 1 more figures