Scientific workflows constitute a pivotal resource for the scientific computing community, as they provide a standard interface to define, execute, and analyze the results of scientific computations. The advent of the Post-Moore era exposes the scientific computing research community with the challenge of scaling HPC facilities, to address the demands of modern scientific applications. While quantum computing, on the one hand, promises noteworthy theoretical speedups for different scientific applications, executing a whole scientific workflow on quantum hardware is infeasible since not all computations can achieve a speedup on quantum hardware. As a consequence, computational scientists defined the concept of a hybrid quantum-classical workflow, which is capable of exploiting the capabilities of quantum and classical hardware. Since the adaptation of classical scientific workflows to the new hybrid quantum-classical scenario can be challenging for scientists, we propose RIGOLETTO, a workflow definition language allowing scientists to define where to execute each task on a hybrid quantum-classical system. As a proof-of-concept, we report on two scientific workflows and identify open challenges in the definition of hybrid quantum-classical workflows.
Scientific workflows constitute a pivotal resource for the scientific computing community, as they provide a standard interface to define, execute, and analyze the results of scientific computations. The advent of the Post-Moore era exposes the scientific computing research community with the challenge of scaling HPC facilities, to address the demands of modern scientific applications. While quantum computing, on the one hand, promises noteworthy theoretical speedups for different scientific applications, executing a whole scientific workflow on quantum hardware is infeasible since not all computations can achieve a speedup on quantum hardware. As a consequence, computational scientists defined the concept of a hybrid quantum-classical workflow, which is capable of exploiting the capabilities of quantum and classical hardware. Since the adaptation of classical scientific workflows to the new hybrid quantum-classical scenario can be challenging for scientists, we propose RIGOLETTO, a workflow definition language allowing scientists to define where to execute each task on a hybrid quantum-classical system. As a proof-of-concept, we report on two scientific workflows and identify open challenges in the definition of hybrid quantum-classical workflows.
As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.
Hybrid quantum-classical workflows combine quantum processing units (QPUs) with classical hardware to address computational tasks that are challenging or infeasible for conventional systems alone. Coordinating these heterogeneous resources at scale demands robust orchestration, reproducibility, and observability. Even in the presence of fault-tolerant quantum devices, quantum computing will continue to operate within a broader hybrid ecosystem, where classical infrastructure plays a central role in task scheduling, data movement, error mitigation, and large-scale workflow coordination.
In this work, we present a cloud-native framework for managing hybrid quantum-HPC pipelines using Kubernetes, Argo Workflows, and Kueue. Our system unifies CPUs, GPUs, and QPUs under a single orchestration layer, enabling multi-stage workflows with dynamic, resource-aware scheduling. We demonstrate the framework with a proof-of-concept implementation of distributed quantum circuit cutting, showcasing execution across heterogeneous nodes and integration of classical and quantum tasks. This approach highlights the potential for scalable, reproducible, and flexible hybrid quantum-classical computing in cloud-native environments.
Scientific knowledge increasingly depends on complex computational processes where both hardware and software layers can influence research outcomes. As computational complexity grows, classical-quantum integration provides a lens for examining how the scientific method adapts, particularly regarding a foundational principle of scientific validation - reproducibility. Building upon previous warnings of an ongoing reproducibility crisis in the computational context, this paper examines challenges across classical (HPC) and quantum computing. Despite its deterministic nature, HPC faces reproducibility threats from hardware dependencies, documentation inadequacies, disincentivizing research culture and infrastructure variation. Quantum computing, at low technological maturity, amplifies some challenges, while creating new ones through probabilistic outputs, hardware-specific noise, and tight software-hardware coupling. Classical-quantum integration reveals a telling pattern, where current reproducibility frameworks prove inadequate, as infrastructure blends with the results. Quantum integration serves as a catalyst exposing methodological limitations across the computational domain. We propose a workflow-centered path forward, pointing to the value of gradual cultural shift toward workflow-centered scientific practice. By developing meta-workflows that document both process abstractions and implementation contexts, we create a more robust foundation for scientific knowledge that acknowledges complexity without sacrificing rigor. The path forward involves embracing this evolution in understanding scientific knowledge rather than resisting it
Hybrid quantum-classical workflows are expected to underpin practical quantum computing applications, yet the quantum and HPC communities lack a shared framework for reasoning about where and when their integration requirements matter most. Such a framework must separate two distinct levels of analysis: the application level, where communication overhead affects runtime performance, and the real-time level, where it determines feasibility. To address this, we introduce a runtime model that decomposes workflow execution into quantum compute, classical compute, and communication costs. At the application level, a communication-to-computation ratio from this decomposition quantifies whether a workflow is communication-bound or compute-bound; at the real-time level, a feasibility constraint determines whether timing requirements can be met at all, with the reaction time setting the logical clock speed of fault-tolerant computation once they are. Application of this model to representative workflows demonstrates that co-location of quantum processors with HPC infrastructure offers negligible performance benefit for compute-intensive applications today, while tight integration remains crucial for real-time tasks such as quantum error correction needed for large scale quantum computations. However, we discuss how even at the application level these assessments may shift with hardware evolution, illustrating how the model can identify specific crossover conditions, and how, under fault tolerance, the reaction time can set application-level performance.
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.
Quantum Computers offer an intriguing challenge in modern Computer Science. With the inevitable physical limitations to Moore's Law, quantum hardware provides avenues to solve grander problems faster by utilizing Quantum Mechanical properties at subatomic scales. These futuristic devices will likely never replace traditional HPC, but rather work alongside them to perform complex tasks, utilizing the best of decades of HPC and quantum computing research. We leverage the capabilities of scientific workflows to make traditional HPC and Quantum Computers work together. To demonstrate this capability, we implemented three algorithms: Grover's Search Algorithm, Shor's Factoring Algorithm, and a 4-node Traveling Salesman Algorithm. The algorithms' implementation and generated inputs are sent from ORNL HPC to IBMQ, the algorithms run on IBMQ, and the results return. The entire process is automated as a workflow by encoding it into the Parsl parallel scripting and workflow platform.