Quantum computing introduces a new computing paradigm that promises to solve problems that cannot be solved by classical computers efficiently. Thus, quantum applications will be more and more integrated in classical applications. To bring these composite applications into production, technologies for an automated deployment and orchestration are required to avoid manual error-prone and time-consuming processes. For non-quantum applications, a variety of deployment technologies have been developed in recent years. However, the deployment of quantum applications currently differs significantly from non-quantum applications and thus, leads to a different modeling procedure for the deployment of quantum applications. To overcome these problems, we propose TOSCA4QC that introduces two deployment modeling styles based on the Topology and Orchestration Specification for Cloud Applications (TOSCA) standard for automating the deployment and orchestration of quantum applications: (i) SDK-specific modeling style to cover all technical deployment details and (ii) SDK-agnostic modeling style supporting common modeling principles. We further show how existing model-driven development (MDD) approach can be applied to refine a SDK-agnostic model to an executable SDK-specific model. We demonstrate the practical feasibility by a prototypical implementation as an extension of the TOSCA ecosystem OpenTOSCA and three case studies with IBMQ and a quantum simulator.
Quantum computing introduces a new computing paradigm that promises to solve problems that cannot be solved by classical computers efficiently. Thus, quantum applications will be more and more integrated in classical applications. To bring these composite applications into production, technologies for an automated deployment and orchestration are required to avoid manual error-prone and time-consuming processes. For non-quantum applications, a variety of deployment technologies have been developed in recent years. However, the deployment of quantum applications currently differs significantly from non-quantum applications and thus, leads to a different modeling procedure for the deployment of quantum applications. To overcome these problems, we propose TOSCA4QC that introduces two deployment modeling styles based on the Topology and Orchestration Specification for Cloud Applications (TOSCA) standard for automating the deployment and orchestration of quantum applications: (i) SDK-specific modeling style to cover all technical deployment details and (ii) SDK-agnostic modeling style supporting common modeling principles. We further show how existing model-driven development (MDD) approach can be applied to refine a SDK-agnostic model to an executable SDK-specific model. We demonstrate the practical feasibility by a prototypical implementation as an extension of the TOSCA ecosystem OpenTOSCA and three case studies with IBMQ and a quantum simulator.
Quantum applications are most often hybrid, i.e., they are not only made of implementations of pure quantum algorithms but also of classical programs as well as workflows and topologies as key artifacts, and data they process. Since workflows and topologies are referred to as “orchestrations” in modern terminology (but with very different meanings), two orchestrations that go hand-in-hand are required to realize quantum applications. We motivate this by means of a nontrivial example, sketch these orchestration technologies, and reveal the overall structure of non-trivial quantum applications. Furthermore, we discuss the implied architecture of a runtime environment for such quantum applications. To validate the introduced architecture, we present a prototypical implementation based on the Camunda workflow engine, its associated modeling tool, as well as the OpenTOSCA ecosystem.
Building quantum applications requires deep knowledge of quantum computing and software engineering. Hence, an abstraction layer reducing the complexity for non-experts is needed. Patterns are an established concept for the abstract description of proven solutions to recurring problems. Therefore, the quantum computing patterns, a pattern language for the quantum computing domain, can be used to define the building blocks and the structure of hybrid quantum applications. Furthermore, concrete software artifacts can be associated with patterns to solve the corresponding problem. However, these software artifacts are usually heterogeneous, e.g., using different data formats. Quantum workflows enable a robust and scalable orchestration of these heterogeneous software artifacts. However, manually modeling and configuring such quantum workflows is a complex, error-prone, and time-consuming task. To overcome this issue, we present an approach that automates the generation and adaptation of quantum workflows using the quantum computing patterns. We provide an architecture realizing our approach, a corresponding prototype, as well as an evaluation comprising different use cases, a runtime comparison, and a user study.
The integration of quantum computing technologies with established classical infrastructure represents a transformative advancement in computational capabilities. This manuscript addresses architectural frameworks enabling seamless orchestration between quantum and classical environments through container-based methodologies. Kubernetes extensions provide standardized interfaces for managing hybrid workloads while ensuring reproducibility across experimental iterations. Custom resource definitions facilitate quantum job specifications with appropriate hardware requirements, while specialized operators handle complex scheduling demands inherent to quantum processing. Implementation architectures demonstrate enhanced resource utilization through dynamic allocation mechanisms that accommodate both paradigms simultaneously. Practical evaluations reveal substantial improvements in workflow automation when orchestration platforms coordinate quantum simulators alongside cloud-based quantum processing units. The extensibility of container-based solutions proves particularly valuable for enterprises navigating evolving quantum hardware landscapes. The technological foundation described establishes a robust framework for quantum-classical integration while addressing security and deployment considerations. Findings indicate strategic advantages in adopting containerized approaches for quantum workflow management, particularly regarding experimental reproducibility and resource optimization across heterogeneous computing environments.
Quantum resources are increasingly integrated into high-performance computing (HPC) and cloud environments, but quantum high-performance computing (QHPC) software stacks remain isolated, often proprietary, full-stack solutions lacking common interfaces across runtime, resource management, orchestration, and execution layers. This paper analyzes nine production QHPC stacks and identifies common design patterns and emerging requirements, covering deployment models, application interaction patterns, SDK support, and readiness for fault-tolerant operation. The survey exposes consistent needs in runtime abstraction, resource management, interconnect semantics, and observability. Based on these findings, we propose the open quantum-HPC software ecosystem ( openQSE) reference architecture as a first step toward unifying the state-of-the-practice. openQSE defines a set of layer boundaries that allow different implementations to interoperate while preserving deployment flexibility, and is structured to support both current noisy intermediate-scale quantum (NISQ) workloads and future fault-tolerant quantum computing (FTQC) systems without changes to upper-layer application interfaces.
Quantum computing is increasingly accessed through cloud-based hybrid environments, but practical adoption depends on more than exposing remote quantum backends. It also requires managed abstractions for orchestrating classical and quantum stages, handling queues, binding backends, and integrating surrounding workflow logic. This paper presents a comprehensive survey of Quantum Serverless from that perspective. Our contribution is twofold: (1) we consolidate the key architectural components of Quantum Serverless into a unified reference architecture; and (2) we evaluate current industry and research offerings through a KPI-driven framework covering ecosystem coverage, service operability, queue handling, pricing flexibility, near-real-time readiness, and production maturity. Using this framework, we analyze 18 quantum cloud platforms and show that the ecosystem is growing but remains unevenly mature. Queue-based execution is already a baseline capability, whereas stronger serverless characteristics, such as Function-as-a-Service, Containers-as-a-Service, near-real-time responsiveness, and production readiness, remain limited. Overall, current Quantum Serverless is best understood as a queue-driven hybrid orchestration model rather than a fully mature cloud-native serverless layer. The survey identifies the main maturity gaps and outlines directions for future development.
This paper introduces a vision for Quantum Software Development lifecycle, proposing a hybrid full-stack iterative model that integrates quantum and classical computing. Addressing the current challenges in Quantum Computing (QC) such as the need for integrating diverse programming languages and managing the complexities of quantum-classical systems, this model is rooted in the principles of DevOps and continuous software engineering. It presents a comprehensive lifecycle for quantum software development, encompassing quantum-agnostic coding, testing, deployment, cloud computing services, orchestration, translation, execution, and interpretation phases. Each phase is designed to accommodate the unique demands of QC, enabling traditional software developers to engage with QC environments without needing in-depth QC expertise. The paper presents a detailed implementation roadmap, utilizing a range of existing tools and frameworks, thereby making quantum software development more accessible and efficient. The proposed model not only addresses current challenges in quantum software development but also makes a substantial contribution to the field of Quantum Software Engineering (QSE). By proposing a structured and accessible model, it sets the stage for further advancements and research in QSE, enhancing its practicality and relevance in a wide range of applications.