Python-based Workflow System for Quantum-HPC Hybrid Application on HPC System and Quantum Computer with Shared Network

The rise of physical quantum computer availability and accessibility to the public increases the interest of researchers in various domains. In order to operate the quantum computer, users have to construct their quantum circuit based on each machine manufacturer development kit. Most of these development kits are developed for Python in favor of accessibility and a user-friendly environment. However, the quantum application is not just a quantum circuit execution. It needs the preparation phase and also the output interpreter phase. Each phase may require different computing resources to work efficiently. We developed a Python-based workflow system to assist users in executing their quantum applications within a rich and diverse computing environment, such as large-scale HPC systems. Our workflow system provides interoperability across heterogeneous platforms by introducing an abstraction layer for job submission, allowing both quantum and HPC workloads to be submitted seamlessly to systems with different job-submission interfaces. This workflow system can divide the application into smaller tasks and allow each task to pair with the suitable computing resources. We conducted the experiments by applying this workflow system with Quantum Phase Estimation, Sample-based Quantum Diagonalization, and Post-Variational Quantum Neural Networks. The results of the experiments demonstrate that each divided task from both applications can be executed on different computing resources efficiently.