QSMEF (Quantum Software Engineering Module Evaluation Framework) is a framework for evaluating the functional contributions of components in quantum software. This software release provides a Python validator, documentation, and reproducible results for five quantum software case studies: SKW quantum walk search, QAOA–MaxCut, TwoLocal–H₂, QPE/Shor, and Minimum Bisection. The framework uses a Hermitian observable to define a functional evaluation metric and Shapley values to attribute contributions to individual components. The validator assesses technical validity and applicability under declared functional constraints. The repository includes experimental results, applicability assessments, and a comparison of the original and modified SKW oracle. Source code: https://github.com/QSMEF/QSMEF-Journal-2026 Version: 1.0.0
QSMEF (Quantum Software Engineering Module Evaluation Framework) is a framework for evaluating the functional contributions of components in quantum software. This software release provides a Python validator, documentation, and reproducible results for five quantum software case studies: SKW quantum walk search, QAOA–MaxCut, TwoLocal–H₂, QPE/Shor, and Minimum Bisection. The framework uses a Hermitian observable to define a functional evaluation metric and Shapley values to attribute contributions to individual components. The validator assesses technical validity and applicability under declared functional constraints. The repository includes experimental results, applicability assessments, and a comparison of the original and modified SKW oracle. Source code: https://github.com/QSMEF/QSMEF-Journal-2026 Version: 1.0.0
QSMEF — Computational Implementation and Reproducibility QSMEF (Quantum Software Engineering Module Evaluation Framework) is a framework for evaluating the functional contributions of blocks in quantum circuits using coalition-based observable measurements and Shapley values, subject to explicitly declared technical, functional, and semantic applicability conditions. This archive provides the computational implementation and supporting research artifacts associated with the QSMEF journal manuscript. The package includes five quantum computing case studies: QAOA–MaxCut, TwoLocal–H₂, the SKW quantum walk, QPE–Shor, and graph partitioning (minimum bisection). These cases illustrate the applicability and limitations of functional attribution under different algorithmic configurations and declared semantic constraints. The archive contains Python source code, dependency specifications, automated regression tests, reference results, execution logs, and documentation. It also includes bibliographic records and analysis materials supporting the literature review of 32 selected studies in Quantum Software Engineering. QSMEF distinguishes technical validity, functional constraints, and executable semantic criteria. The framework verifies formalizable conditions across circuit coalitions, while recognizing that satisfying these conditions does not independently establish complete semantic comparability. The regression tests support computational consistency and reproducibility against previously recorded reference results. They do not constitute independent external validation of the framework or its scientific conclusions. This release is intended to support transparency, traceability, reproducibility, and further research on component-level functional evaluation in quantum software engineering.
QSMEF — Quantum Software Engineering Module Evaluation Framework (v1.1.0) QSMEF is a computational framework for evaluating the functional contributions of quantum circuit components using cooperative game theory and Shapley values. The implementation provides a modular architecture that separates case-study construction from a general applicability validation procedure. Each case is represented through a common computational specification, including the initial quantum state, functional blocks, execution schedule, Hermitian observable, functional constraints, and executable semantic contract. The framework supports coalition evaluation through identity neutralization of absent functional blocks while preserving the original execution order. It evaluates technical conditions, functional constraints, and declared semantic invariants before determining whether functional attribution is applicable. For applicable configurations, QSMEF computes Shapley values and numerically verifies the efficiency property. The implementation also includes an optional granularity assistant for evaluating alternative functional block groupings. The software includes five computational case studies: SKW quantum walk search, QAOA-MaxCut, TwoLocal-H₂, QPE/Shor, and Minimum Bisection. Semantic validation is limited to the declared executable criteria and does not independently establish complete semantic comparability across coalitions. This release supports the computational implementation and reproducibility of the QSMEF journal article.
La Ingeniería de Software Cuántico (Q-SE) requiere herramientas que permitan analizar de manera reproducible y cuantitativa el comportamiento funcional de algoritmos cuánticos, más allá de su corrección global. En este trabajo se presenta SMEF (Software Engineering Module Evaluation Framework), un marco de atribución funcional basado en valores de Shapley, orientado a cuantificar la contribución de bloques funcionales dentro de una implementación cuántica bajo métricas explícitas definidas por el analista. El marco evalúa configuraciones parciales del circuito que preservan el orden físico de ejecución, permitiendo descomponer el comportamiento global en contribuciones funcionales atribuibles a cada bloque, en coherencia con la semántica operacional del algoritmo y con las propiedades axiomáticas del mecanismo de atribución empleado. SMEF no busca reinterpretar la dinámica cuántica subyacente, sino proporcionar métricas funcionales reproducibles y comparables, útiles para actividades propias de la Q-SE, tales como auditoría, validación, comparación sistemática de implementaciones y detección de anomalías funcionales. La propuesta se valida mediante dos casos de estudio: (i) la búsqueda sobre hipercubos del algoritmo SKW y (ii) la etapa de estimación de fase (QPE) del algoritmo de Shor. En ambos casos, los perfiles de contribución obtenidos resultan coherentes con el rol funcional esperado de los bloques analizados y permiten identificar desviaciones funcionales sin necesidad de inspeccionar el circuito a nivel de compuertas.
We present qlbm , a Python software package designed to facilitate the development, simulation, and analysis of Quantum Lattice Boltzmann Methods (QBMs). qlbm is a modular framework that introduces a quantum component abstraction hierarchy tailored to the implementation of novel QBMs. The framework interfaces with state-of-the-art quantum software infrastructure to enable efficient simulation and validation pipelines, and leverages novel execution and pre-processing techniques that significantly reduce the computational resources required to develop quantum circuits. We demonstrate the versatility of the software by showcasing multiple QBMs in 2D and 3D with complex boundary conditions, integrated within automated benchmarking utilities. Accompanying the source code are extensive test suites, thorough online documentation resources, analysis tools, visualization methods, and demos that aim to increase the accessibility of QBMs while encouraging reproducibility and collaboration. Program summary Program Title: qlbm CPC Library link to program files: https://doi.org/10.17632/28hkvsg7p2.1 Developer's repository link: https://github.com/QCFD-Lab/qlbm Licensing provisions: MPL-2.0 Programming language: Python3 Supplementary material: The documentation of is available at https://qcfd-lab.github.io/qlbm/ . Nature of problem: The advent of quantum algorithms for computational fluid dynamics brings with it challenges that are new to the established field of computational physics. These challenges include the lack of standardized implementations of the still nascent quantum methods, the intense computational demands of developing and simulating quantum algorithms on hardware available today, and the absence of tools that integrate novel developments into established infrastructure. Because of these current limitations, physicists and mathematicians expend superfluous resources on tasks that more mature computational physics branches have surmounted long ago. Solution method: QLBM is a software package that provides an end-to-end development environment for quantum lattice Boltzmann methods. The modular design and flexible quantum circuit library provide a base for extending and generalizing quantum algorithms. Performance enhancements exploit the paradigm of quantum computing simulations to accelerate the speed at which researchers can verify the validity of their methods. Its integration with state-of-the-art quantum computing software and visualization tools increases the algorithms' accesibility. These features allow QLBM to effectively generate, simulate, and analyze quantum circuits for 2D and 3D computational fluid dynamics problems.
Quantum computing promises transformative computational capabilities; however, the absence of structured and standardized software engineering significantly limits its practical scalability. Quantum Software Engineering (QSE) remains fragmented, tool-centric, and largely experimental. This paper proposes a structured Quantum Software Engineering Lifecycle Framework that systematically integrates requirements engineering, hybrid design, quantum development, verification, deployment, and governance. To validate the proposed lifecycle, a two-stage evaluation was conducted. First, a Delphi-based expert validation involving 15 domain experts assessed clarity, feasibility, scalability, and hybrid applicability. Second, a simulation-based comparative analysis using Qiskit and PennyLane evaluated the lifecycle against ad-hoc development workflows across variational and search-based quantum algorithms. Results demonstrate a 24% reduction in development time, 15–18% improvement in execution fidelity, and significant gains in modularity, reusability, and tool interoperability. These findings confirm that adopting a structured lifecycle enables more reliable, scalable, and sustainable quantum software development, positioning QSE as a mature engineering discipline rather than purely experimental practice.
Quantum Software Engineering (QSE) is emerging as a critical discipline to make quantum computing accessible to a broader developer community; however, most quantum development environments still require developers to engage with low-level details across the software stack—including problem encoding, circuit construction, algorithm configuration, hardware selection, and result interpretation—making them difficult for classical software engineers to use. To bridge this gap, we present C2 ∣Q⟩ , a hardware-agnostic quantum software development framework that translates specific types of classical specifications into quantum-executable programs while preserving methodological rigor. The framework applies modular software engineering principles by classifying the workflow into three core modules: an encoder that classifies problems, produces Quantum-Compatible Formats (QCFs), and constructs quantum circuits, a deployment module that generates circuits and recommends hardware based on fidelity, runtime, and cost, and a decoder that interprets quantum outputs into classical solutions. This architecture supports systematic evaluation across simulators and Noisy Intermediate-Scale Quantum (NISQ) quantum devices, remaining scalable to new problem classes and algorithms. In evaluation, the encoder module achieved a 93.8% completion rate, the hardware recommendation module consistently selected the appropriate quantum devices for workloads scaling up to 56 qubits. End-to-end experiments on 434 Python programs and 100 JSON problem instances demonstrate that the full C2 ∣Q⟩ workflow executes reliably on simulators and can be deployed successfully on representative real quantum hardware, with empirical runs limited to small- and medium-sized instances consistent with current NISQ capabilities. A proxy-based usability analysis further indicates substantial reductions in handwritten lines of code and explicit configuration steps compared to conventional quantum SDK workflows. These results indicate that C2 ∣Q⟩ lowers the entry barrier to quantum software development by providing a reproducible, extensible toolchain that connects classical specifications to quantum execution. The open-source implementation of C2 ∣Q⟩ is available at https://github.com/C2-Q/C2Q and as a ready-to-use Python package at https://pypi.org/project/c2q-framework/ .