Scaling Hybrid Quantum-HPC Applications with the Quantum Framework

2025-09-17

AI Summary

Quantum applications in the NISQ era face challenges in scalable and reproducible execution across heterogeneous backends—including local simulators and cloud-based hardware. Method: This paper proposes a high-performance computing (HPC)-aware modular quantum framework featuring a backend-agnostic orchestration architecture that supports both non-variational and variational hybrid quantum–HPC workflows. It dynamically schedules quantum circuits—based on structural properties, entanglement metrics, and circuit depth—to diverse backends including Qiskit Aer, NWQ-Sim, QTensor, TN-QVM, and IonQ, enabling distributed concurrent solving of subproblems. Contribution/Results: Experimental evaluation demonstrates strong scalability and cross-platform consistency on large-scale Ising model simulations and combinatorial optimization tasks. The framework significantly improves benchmark fairness, enables scientifically grounded backend selection, and enhances the identification of quantum advantage.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Scaling hybrid quantum-HPC applications on NISQ devices
• Integrating diverse quantum simulators and hardware backends
• Enabling flexible backend-agnostic execution for quantum advantage

Innovation

• Modular HPC-aware orchestration layer
• Unified interface for multiple backends
• Distributed concurrent subproblem execution