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arXiv:2610.39406

QSoftCore: A Conceptual Foundation for Quantum Software Engineering

Tao Yue, Yuechen Li, Man Zhang

cs.SEquant-phSubmitted Oct 29, 2026• Announced Oct 30, 2026• 0 citations
arXivHTMLPDF

Abstract

Quantum software engineering (QSE) requires understanding application requirements, quantum programs and algorithms, quantum computation paradigms, executions, and deployment. However, relevant quantum computing (QC) concepts are often defined across physical, mathematical, and paradigm-specific levels of abstraction, making it difficult for the QSE community and hence proposed methods to refer to them consistently. To this end, we present QSoftCore, a conceptual model that reorganizes established QC concepts from a software engineering perspective and connects quantum applications, computational problems, algorithms, configurations, programs, and paradigm-specific computation specifications with state evolution, execution results, and backends. Our assessment against QSE literature across key software engineering activities indicates that QSoftCore provides shared QC referents for quantum computations, executions, deployment configurations, backends, and measurement evidence across the QSE lifecycle.

AI Summary

Quantum software engineering uses concepts drawn from mathematical, physical, and platform-specific descriptions of quantum computing. This paper asks how those concepts can be organized into a shared vocabulary connecting application requirements to computation design, deployment, and execution evidence. QSoftCore is a conceptual model expressed through UML class diagrams, organized into five views: applications and software systems; computation paradigms; gate-based computation; execution and evolution; and deployment. It distinguishes reusable algorithms from application-specific configurations, programs from computation specifications, and specifications from execution attempts. It covers circuits, analog Hamiltonian evolution, quantum annealing, and measurement-based computation, with more detail for gate-based systems. Execution concepts separate expected outcome probabilities, observed measurement statistics, inferred estimates, simulator state snapshots, and error-mitigated results. Deployment concepts record backend-specific artifacts, physical mappings, calibration context, and noise assumptions. Two worked examples map hybrid robot navigation and test-suite optimization onto these distinctions. The assessment qualitatively maps concepts used in selected QSE literature across requirements, architecture, code generation, deployment, and quality assurance to the model. It reports coverage of the reviewed works’ quantum-computing concepts and identifies their predominantly circuit-centered focus. There are no quantitative benchmarks or demonstrated improvements in engineering outcomes. The model explicitly reorganizes established concepts rather than introducing a quantum formalism. It is incomplete by design, gives unequal detail to different paradigms, and leaves task-specific software-engineering artifacts, implementation-level control flow, and detailed hardware characterization outside its scope. Its stated role is to support, not replace, existing engineering methods.

Some signs of AI-generated textIncrementalOverclaims

Error found: The supplied source contains seven caption-only figure environments, leaving the UML diagrams and referenced OCL constraints unavailable for checking the full conceptual specification. The paper defines decision problems as Boolean-output problems but classifies unrestricted learning labels and navigation action choices as decision outputs, contradicting its own classification.

Written by gpt-6.1-sol from the paper's full text. Automated: it can be wrong.

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Quantum software engineering (QSE) requires understanding application requirements, quantum programs and algorithms, quantum computation paradigms, executions, and deployment. However, relevant quantum computing (QC) concepts are often defined across physical, mathematical, and paradigm-specific levels of abstraction, making it difficult for the QSE community and hence proposed methods to refer to them consistently. To this end, we present QSoftCore, a conceptual model that reorganizes established QC concepts from a software engineering perspective and connects quantum applications, computational problems, algorithms, configurations, programs, and paradigm-specific computation specifications with state evolution, execution results, and backends. Our assessment against QSE literature across key software engineering activities indicates that QSoftCore provides shared QC referents for quantum computations, executions, deployment configurations, backends, and measurement evidence across the QSE lifecycle.
Quantum software engineering uses concepts drawn from mathematical, physical, and platform-specific descriptions of quantum computing. This paper asks how those concepts can be organized into a shared vocabulary connecting application requirements to computation design, deployment, and execution evidence.
QSoftCore is a conceptual model expressed through UML class diagrams, organized into five views: applications and software systems; computation paradigms; gate-based computation; execution and evolution; and deployment. It distinguishes reusable algorithms from application-specific configurations, programs from computation specifications, and specifications from execution attempts. It covers circuits, analog Hamiltonian evolution, quantum annealing, and measurement-based computation, with more detail for gate-based systems. Execution concepts separate expected outcome probabilities, observed measurement statistics, inferred estimates, simulator state snapshots, and error-mitigated results. Deployment concepts record backend-specific artifacts, physical mappings, calibration context, and noise assumptions. Two worked examples map hybrid robot navigation and test-suite optimization onto these distinctions.
The assessment qualitatively maps concepts used in selected QSE literature across requirements, architecture, code generation, deployment, and quality assurance to the model. It reports coverage of the reviewed works’ quantum-computing concepts and identifies their predominantly circuit-centered focus. There are no quantitative benchmarks or demonstrated improvements in engineering outcomes.
The model explicitly reorganizes established concepts rather than introducing a quantum formalism. It is incomplete by design, gives unequal detail to different paradigms, and leaves task-specific software-engineering artifacts, implementation-level control flow, and detailed hardware characterization outside its scope. Its stated role is to support, not replace, existing engineering methods.
The quantum computing (QC) field is rapidly moving beyond the realm of pure science to become a commercially viable technology that may be able to overcome the drawbacks of traditional computing. Major technology tycoons have spent in building coding frameworks and hardware to create applications specifically designed for quantum computing over the last few years. The development of QC hardware is accelerating, however, the requirement for software-intensive methodology, approaches, procedures, instruments, roles and responsibilities for creating industrial-focused quantum software applications arises from operationalizing the QC. This paper outlines the concept of quantum software engineering (QSE) life cycle, which entails the engineering of quantum requirements, design, implementation, testing and maintenance of quantum software. This paper notably advocates for collaborative efforts between the industrial community and software engineering research to propose practical solutions to support the complete activities for the development of quantum software. The proposed vision makes it easier for researchers and practitioners to suggest new procedures, reference designs, cutting-edge equipment, and methods for utilizing quantum computers and creating the newest and most advanced quantum software.
The principles of quantum mechanics – superposition, entanglement, measurement, and decoherence – form the foundation of quantum computing. Qubits, which are abstract objects having a mathematical expression to implement the rules of quantum physics, are the fundamental building blocks of computation. Software is a key component of quantum computing, along with quantum hardware. Algorithms make up software, and they are implemented using logic gates and quantum circuits. These qualities make quantum computing a paradigm that non-physicists find difficult to comprehend. It is crucial to incorporate a conceptual framework of the principles upon which quantum computing is founded into this new method of creating software. In this paper, we present a kind of taxonomical view of the fundamental concepts of quantum computing and the derived concepts that integrate the emerging discipline of quantum software engineering. Because the systematic review's main goal is to identify the core ideas behind quantum computing and quantum software, we conducted a quasi-systematic mapping as part of the review process. The findings can serve as a starting point for computer science teachers and students to address the study of this field.
Abstract Quantum computing is based on the principles of quantum mechanics, such as superposition, entanglement, measurement, and decoherence. The basic units of computation are qubits, which are abstract objects with a mathematical expression to implement the quantum mechanics principles. Alongside quantum hardware, software is a principal element for conducting quantum computing. The software consists of logic gates and quantum circuits that implement algorithms for the execution of quantum programs. Due to those characteristics, quantum computing is a paradigm that non-physics experts cannot understand. Under this new scheme for developing software, it is important to integrate a conceptual framework of the fundamentals on which quantum computing is based. In this paper, we present a kind of taxonomical view of the fundamental concepts of quantum computing and the derived concepts that integrate the emerging discipline of quantum software engineering. We performed a quasi-systematic mapping for conducting the systematic review because the objective of the review only intends to detect the fundamental concepts of quantum computing and quantum software. The results can help computer science students and professors as a starting point to address the study of this discipline.
Quantum computing is an emerging paradigm that enables to solve a variety of problems more efficiently than it is possible on classical computers. As the first quantum computers are available, quantum algorithms can be implemented and executed on real quantum hardware. However, the capabilities of today’s quantum computers are very limited and quantum computations are always disturbed by some error. Thus, further research is needed to develop or improve quantum algorithms, quantum computers, or required software tooling support. Due to the interdisciplinary nature of quantum computing, a common understanding of how to develop and execute a quantum software application is needed. However, there is currently no methodology or lifecycle comprising all relevant phases that can occur during the development and execution process. Hence, in this paper, we introduce the quantum software lifecycle consisting of ten phases a gate-based quantum software application should go through. We analyze the purpose of each phase, the available methods and tools that can be applied, and the open problems or research questions. Therefore, the lifecycle can be used as a baseline for discussions and future research.
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.
As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE.