Quantum computing has the potential to reshape scientific research in areas like materials science, chemistry, biology, and more. But many researchers still face hurdles, especially when trying to integrate quantum tools into existing analysis pipelines. In this paper, we explore how the Galaxy Project, a platform known for enabling reproducible computational research, can be extended to support hybrid quantum-classical workflows. By combining Galaxy's user-friendly, provenance-aware environment with quantum programming tools, we offer scientists a practical way to build and share workflows that include both quantum and classical components. This GalaxyQ approach lowers the entry barrier for quantum computing and promotes transparency, collaboration, and reproducibility in scientific computing.
Quantum computing has the potential to reshape scientific research in areas like materials science, chemistry, biology, and more. But many researchers still face hurdles, especially when trying to integrate quantum tools into existing analysis pipelines. In this paper, we explore how the Galaxy Project, a platform known for enabling reproducible computational research, can be extended to support hybrid quantum-classical workflows. By combining Galaxy's user-friendly, provenance-aware environment with quantum programming tools, we offer scientists a practical way to build and share workflows that include both quantum and classical components. This GalaxyQ approach lowers the entry barrier for quantum computing and promotes transparency, collaboration, and reproducibility in scientific computing.
We present QDK/Chemistry, a software toolkit for quantum chemistry workflows targeting quantum computers. The toolkit addresses a key challenge in the field: while quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted algorithms in the quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source quantum chemistry packages and quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible quantum chemistry experiments.
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
We present a hybrid quantum-classical simulation framework inspired by the architectural principles of large language models (LLMs). This system integrates offline engines, cloud-based orchestration, and multi-user synchronization to perform high-performance scientific simulations, including quantum-classical state evolution, harmonic resonance analysis, and RNA repair modeling. By adapting LLM concepts such as embeddings, iterative feedback loops, and layered processing, we extend their application beyond natural language to dynamic scientific computation. The framework supports reproducible simulations, real-time state aggregation, and scalable collaboration, offering a generalized architecture for hybrid computation research and applied scientific workflows.
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.
A critical challenge to making quantum computers work in practice is effectively combining them with classical computing resources. From the classical side of hybrid algorithms and integrated application workflows to decoding syndromes for quantum error correction, tightly coupled high performance classical computing will be important for many of the functions required to realize useful quantum computing. A key tool for enabling research and application development is a programming model and software toolchain which allow researchers to straightforwardly co-program classical and quantum computers and leverage the best tools available for each. NVIDIA CUDA Quantum is a single-source programming model in C++ and Python for heterogeneous quantum-classical computing. The CUDA Quantum platform provides several advantages and new capabilities that enable users to get more out of quantum processors. Here, we present CUDA Quantum and demonstrate several use cases including Variational Quantum Eigensolver (VQE) where it provides a significant (287x) performance and capability benefit over existing quantum programming.
Abstract Quantum applications are hybrid, i.e., they comprise quantum and classical programs, which must be orchestrated. Workflows are a proven solution for orchestrating heterogeneous programs while providing benefits, such as robustness or scalability. However, the orchestration using workflows can be inefficient for some quantum algorithms, requiring the execution of quantum and classical programs in a loop. Hybrid runtimes are offered to efficiently execute these algorithms. For this, the quantum and classical programs are combined in a single hybrid program, for which the execution is optimized. However, this leads to a conceptual gap between the modeling benefits of workflow technologies, e.g., modularization, reuse, and understandability, and the efficiency improvements when using hybrid runtimes. To close this gap, we introduce a method to model all tasks explicitly in the workflow model and analyze the workflow to detect parts of the workflow that can benefit from hybrid runtimes. Furthermore, corresponding hybrid programs are automatically generated based on the quantum and classical programs, and the workflow is rewritten to invoke them. To ease the live monitoring and later analysis of workflow executions, we integrate process views into our method and collect related provenance data. Thus, the user can visualize and monitor the workflow in the original and rewritten form within the workflow engine. The practical feasibility of our approach is validated by a prototypical implementation, a case study, and a runtime evaluation.