In today’s software engineering, iterations, affordable en masse, form an important part of just about any system. However, not all computing resources are cheap to consume. In High-Performance (HPC) and Quantum Computing (QC), executions can consume considerable amounts of energy and time, which is reserved and used even if the very first steps in the process fail. This means that developers must assume a different attitude towards programming, and aim at error-free software before its execution. This is commonly facilitated using simulators, which are commonplace for both HPC and QC. However the fashion developers advance from one tool to another is ad-hoc, with no established software engineering guidelines, and the final step from simulators to HPC/QC is still a leap of faith, comparable to releasing software. In this paper, we propose a vision where developers can iterate in an agile fashion when developing quantum software. The iterations are defined such that when the solution is still vague in the beginning, computations are interactive and provide instant feedback, thus supporting conceptualization of the software and experimenting with new ideas. When the solution becomes more precise, more expensive computations such as quantum algorithm and hyperparameter optimization are executed in batches.
In today’s software engineering, iterations, affordable en masse, form an important part of just about any system. However, not all computing resources are cheap to consume. In High-Performance (HPC) and Quantum Computing (QC), executions can consume considerable amounts of energy and time, which is reserved and used even if the very first steps in the process fail. This means that developers must assume a different attitude towards programming, and aim at error-free software before its execution. This is commonly facilitated using simulators, which are commonplace for both HPC and QC. However the fashion developers advance from one tool to another is ad-hoc, with no established software engineering guidelines, and the final step from simulators to HPC/QC is still a leap of faith, comparable to releasing software. In this paper, we propose a vision where developers can iterate in an agile fashion when developing quantum software. The iterations are defined such that when the solution is still vague in the beginning, computations are interactive and provide instant feedback, thus supporting conceptualization of the software and experimenting with new ideas. When the solution becomes more precise, more expensive computations such as quantum algorithm and hyperparameter optimization are executed in batches.
Despite the increasing interest in quantum computing, the aspect of development to achieve cost-effective and reliable quantum software applications has been slow. One barrier is the software engineering of quantum programs, which can be approached from two directions. On the one hand, many software engineering practices, debugging in particular, are bound to classical computing. On the other hand, quantum programming is closely associated with the phenomena of quantum physics, and consequently, the way we express programs resembles the early days of programming. Moreover, much of the software engineering research today focuses on agile development, where computing cycles are cheap and new software can be rapidly deployed and tested, whereas in the quantum context, executions may consume lots of energy, and test runs may require lots of work to interpret. In this paper, we aim at bridging this gap by starting with the quantum computing workflow and by mapping existing software engineering research to this workflow. Based on the mapping, we then identify directions for software engineering research for quantum computing.
With quantum computing (QC) maturing, high-performance computing (HPC) centers are already preparing to host early-phase production versions of such systems. Unlike their experimental predecessors in physics laboratories, with a very small and dedicated user community, this next generation of systems needs to serve a wider user community and must work in concert with existing HPC systems and software stacks. This article describes our vision for an integrated ecosystem that combines existing HPC and evolving quantum software stacks into a single system to enable a common and continuous user experience. This integration comes with several major challenges as quantum systems pose significantly different requirements including increased need for compilation at run time, long optimization times, statistical evaluations of results, and the need to work with few centralized resources. To overcome these challenges, new scheduling approaches on the HPC side and new programming approaches on the QC side are required.
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.
The understanding of the Agile development practices and quantum computer are becoming more and more a requirement as the engineering projects of the day are growing extremely complicated and dynamic. Agile methods that are familiar to facilitate an iterative cycle, cooperate and adaptability are inherently appropriate to the experimental and unpredictable world of quantum software development. Quantum computing (QC) that is grounded on the principles of superposition and entanglement is the origin of novel computational paradigms that require new programming and continuous fine-tuning. In addition to satisfying this need for brief development cycles and quick feedback loops, other Agile methods like Scrum, Kanban, and Extreme Programming (XP) also demand rigorous planning. On the other hand, quantum software engineering is faced with specific issues such as the unskilled personnel, testing issues, and hardware limitations. In that case, the Agile methodologies are salvaged and assist in the breaking down of complex and quantum tasks into manageable units and result in a continuous improvement. All in all, standardization of Agile practices and quantum computing does not just assist in the innovation, but also in reduction of the risk of development and speeding up the development of the practical quantum solutions to the current engineering projects.
Quantum computing (QC) introduces a novel mode of computation with the possibility of greater computational power that remains to be exploitedpresenting exciting opportunities for high-performance computing (HPC) applications. However, recent advancements in the field have made clear that QC does not supplant conventional HPC, but can rather be incorporated into current heterogeneous HPC infrastructures as an additional accelerator, thereby enabling the optimal utilization of both paradigms. The desire for such integration significantly affects the development of software for quantum computers, which in turn influences the necessary software infrastructure. To date, previous review articles have investigated various quantum programming tools (QPTs) (such as languages, libraries, frameworks) in their ability to program, compile, and execute quantum circuits. However, the integration effort with classical HPC frameworks or systems has not been addressed. This study aims to characterize existing QPTs from an HPC perspective, investigating if existing QPTs have the potential to be efficiently integrated with classical computing models and determining where work is still required. This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for a high-performance computing quantum computing (HPCQC) environment.
Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical high-performance computing are used. Instead, quantum simulations map properties and characteristics of subject systems, for instance chemical molecules, onto quantum devices that then mimic the system under study.
Currently, the use of these techniques is largely limited to fundamental science, as the overall approach remains tailored for specific problems: We lack infrastructure and modelling abstractions that are provided by the software engineering community for other computational domains.
In this paper, we identify critical gaps in the quantum simulation software stack-particularly the absence of general-purpose frameworks for model specification, Hamiltonian construction, and hardware-aware mappings. We advocate for a modular model-driven engineering (MDE) approach that supports different types of quantum simulation (digital and analogue), and facilitates automation, performance evaluation, and reusability. Through an example from high-energy physics, we outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation workflows.