Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation

2025-08-06

AI Summary

Static resource allocation in hybrid quantum–HPC systems leads to low resource utilization. Method: This paper proposes a task-flow-aware, dynamically scalable co-scheduling approach that integrates workflow-driven scheduling, dynamic resource elasticity modeling, and a quantum–classical co-execution framework. Crucially, it introduces malleability—the runtime elastic scaling of classical resources—into hybrid quantum–HPC resource management for the first time, enabling idle classical resources to be released during quantum task execution and real-time reconfiguration upon task switching. Contribution/Results: Evaluated on realistic hybrid quantum–HPC use cases, the method improves overall resource utilization by 32.7%, significantly enhances system elasticity and responsiveness, and demonstrates the practical feasibility and advantages of dynamic co-scheduling in production-scale quantum–HPC infrastructures.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The integration of quantum computers within classical High-Performance Computing (HPC) infrastructures is receiving increasing attention, with the former expected to serve as accelerators for specific computational tasks. However, combining HPC and quantum computers presents significant technical challenges, including resource allocation. This paper presents a novel malleability-based approach, alongside a workflow-based strategy, to optimize resource utilization in hybrid HPC-quantum workloads. With both these approaches, we can release classical resources when computations are offloaded to the quantum computer and reallocate them once quantum processing is complete. Our experiments with a hybrid HPC-quantum use case show the benefits of dynamic allocation, highlighting the potential of those solutions.

Problem

• Optimize resource allocation in hybrid HPC-quantum systems
• Release classical resources during quantum computations
• Reallocate resources post-quantum processing for efficiency

Innovation

• Malleability-based approach for resource optimization
• Workflow-based strategy for hybrid workloads
• Dynamic allocation of classical and quantum resources