The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm.
The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm.
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 has the potential to significantly impact many application domains, as several quantum algorithms are promising to solve problems more efficiently than possible on classical computers. However, various complex pre- and post-processing tasks have to be performed when executing a quantum circuit, which require immense mathematical and technical knowledge. For example, calculations on today's quantum computers are noisy and require an error mitigation task after the execution. Hence, integrating classical applications with quantum circuits is a difficult challenge. In this paper, we introduce a modeling extension for imperative workflow languages to enable the integration of quantum computations and ease the orchestration of classical applications and quantum circuits. Further, we show how the extension can be mapped to native modeling constructs of extended workflow languages to retain the portability of the workflows. We validate the practical feasibility of our approach by applying our proposed extension to BPMN and introduce Quantum4BPMN.
Current quantum computers are still error-prone, with measurement errors being one of the factors limiting the scalability of quantum devices. To reduce their impact, a variety of readout error mitigation methods, mostly relying on classical post-processing, have been developed. However, the application of these methods is complicated by their heterogeneity and a lack of information regarding their functionality, configuration, and integration. To facilitate their use, we provide an overview of existing methods, and evaluate general and method-specific configuration options. Quantum applications comprise many classical pre- and post-processing tasks, including readout error mitigation. Automation can facilitate the execution of these often complex tasks, as their manual execution is time-consuming and error-prone. Workflow technology is a promising candidate for the orchestration of heterogeneous tasks, offering advantages such as reliability, robustness, and monitoring capabilities. In this paper, we present an approach to abstractly model quantum workflows comprising configurable readout error mitigation tasks. Based on the method configuration, these workflows can then be automatically refined into executable workflow models. To validate the feasibility of our approach, we provide a prototypical implementation and demonstrate it in a case study from the quantum humanities domain.
With the rapid advancement of quantum technologies, the integration between classical and quantum computing systems is an active area of research critical to future development. The coupling between these systems requires both to be as efficient as possible. One of the key elements to increase efficiency on the quantum side is circuit optimization. The goal is to execute the circuit on the desired hardware in less time and with less complexity, thereby reducing the impact of noise on the quantum system. However, the optimization process does not guarantee to generate improved results, yet it is always a computationally highly complex task that can create significant load for the classical computing side. To mitigate this problem, we propose a novel approach to predict the optimizability of any circuit using a Machine Learning-based algorithm within the decision workflow. This optimizes the most suitable circuits thereby increasing efficiency of the optimization process itself.
Quantum computing promises revolutionary advancements across various fields, but its practical realization hinges on overcoming significant challenges in algorithm execution. Traditional hardware-software co-design approaches for quantum computing often rely on pre-defined architectures and compilation strategies, which can be suboptimal for diverse quantum algorithms. This paper proposes an adaptive hardware-software co-design framework that dynamically adjusts the quantum computing system's configuration – encompassing both hardware architecture and software compilation – in response to the specific requirements of the algorithm being executed. The core claim is that optimized co-design is pivotal for efficient quantum algorithm execution. This framework leverages a dynamic optimization loop, continuously monitoring algorithm performance and adjusting parameters such as qubit connectivity, gate set selection, and compilation strategies. The system utilizes a mathematical model to represent the algorithm's computational demands and hardware capabilities, enabling real-time adaptation. This approach moves beyond static design methodologies, offering a pathway towards significantly improved performance and resource utilization in quantum computing systems. The framework's ability to adapt in real-time represents a crucial step towards unlocking the full potential of quantum algorithms.