Scaling Hybrid Quantum-HPC Applications with the Quantum Framework

Abstract

Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NISQ) devices. These workflows must operate seamlessly across diverse simulators and hardware backends since no single simulator offers the best performance for every circuit type. Simulation efficiency depends strongly on circuit structure, entanglement, and depth, making a flexible and backend-agnostic execution model essential for fair benchmarking, informed platform selection, and ultimately the identification of quantum advantage opportunities. In this work, we extend the Quantum Framework (QFw), a modular and HPC-aware orchestration layer, to integrate multiple local backends (Qiskit Aer, NWQ-Sim, QTensor, and TN-QVM) and a cloud-based quantum backend (IonQ) under a unified interface. Using this integration, we execute a number of non-variational as well as variational workloads. The results highlight workload-specific backend advantages: while Qiskit Aer's matrix product state excels for large Ising models, NWQ-Sim not only leads on large-scale entanglement and Hamiltonian but also shows the benefits of concurrent subproblem execution in a distributed manner for optimization problems. These findings demonstrate that simulator-agnostic, HPC-aware orchestration is a practical path toward scalable, reproducible, and portable Q-HPC ecosystems, thereby accelerating progress toward demonstrating quantum advantage.

9 pages, 5 figures

References in corpus (14)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• A variational eigenvalue solver on a quantum processor
• Charge insensitive qubit design derived from the Cooper pair box
• Quantum algorithm for solving linear systems of equations
• Quantum algorithms: an overview
• Hamiltonian Simulation by Qubitization
• The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size
• Variational Quantum Linear Solver
• Machine Learning Phase Transitions with a Quantum Processor
• The MQT Handbook: A Summary of Design Automation Tools and Software for Quantum Computing
• Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs
• Bridging Paradigms: Designing for HPC-Quantum Convergence
• SCIM MILQ: An HPC Quantum Scheduler