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.
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.
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 meaning), two orchestrations that go hand-in-hand are required to realize quantum applications. We motivate this by means of a non-trivial example, sketch these orchestration technologies and reveal the overall structure of nontrivial quantum applications as well as the implied architecture of a runtime environment for such applications.
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.
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.
Quantum computing is a new emerging technology which is capable of performing specific types of calculations more efficiently than classical computers. Yet quantum computers have their own limitations in terms of noise, decoherence, and qubits numbers, so the current research focus on hybrid quantum-classical computation where classical computers work in conjunction with quantum devices to perform computation. This chapter discusses the architecture of quantum-ready clouds and describes how to effectively run a hybrid quantum-classical workflows over cloud environment. It introduces the middleware and Application Programming Interface (API) in order to support communication between cloud environment and quantum hardware. Furthermore, the chapter shows how the orchestration mechanism to coordinate the classical cloud resources and quantum processors, and presents a simple workflow model for data routing, processing, and analysis utilizing classical/cloud and quantum hardware.
Quantum computing has the potential to revolutionize the way we solve complex problems in various fields. Its unique features, such as superposition and entanglement, allow for speedup in certain computations compared to classical computers. In this paper, we show that incorporating quantum computing into a heterogeneous computation architecture is possible and potentially can greatly enhance the overall performance and efficiency of solving a wide range of problems. We propose a heterogeneous computation architecture that integrates quantum computing and classical computing. We describe how this architecture can be implemented using widely available opensource software and provide a reference implementation. We also discuss potential benefits and challenges. Our goal is to highlight the importance of seamless inclusion of quantum computing in the future of computation and how it can be achieved with tools we already have.
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.