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 — 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.
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
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/ .
Variational quantum algorithms are hybrid quantum-classical workflows whose results depend on many interacting choices, including the ansatz, Hamiltonian, optimizer, backend, shot count, noise model, mitigation method, random seed, stopping criteria, and software versions. In current practice, this information is often scattered across code, configuration files, logs, backend metadata, and paper descriptions, making executions difficult to reproduce, compare, debug, and reuse. This paper proposes an ontology-supported framework for representing Variational Quantum Algorithm (VQA) execution records as structured and machine-readable software engineering artifacts. The framework defines a Web Ontology Language (OWL) ontology for modeling the main entities involved in VQA experimentation, including algorithms, circuits, ansatzes, Hamiltonians, optimizers, backends, noise models, mitigation techniques, execution steps, software environments, measurement outcomes, and results. It further combines the ontology with Shapes Constraint Language (SHACL) constraints for validating completeness and consistency, and SPARQL Protocol and RDF Query Language (SPARQL) competency queries for retrieving reproducibility-relevant information. We demonstrate the approach using Variational Quantum Eigensolver (VQE) execution records, including a valid record and intentionally incomplete or inconsistent examples. The results show that the framework can represent complete VQA execution contexts, detect missing or malformed metadata, and support query-based inspection of information needed for reproducible quantum software experimentation.
This record presents the scientific validation of QCOS (Quantum Circuit Optimization System), a reproducible and test-driven orchestration framework for variational quantum algorithms in the NISQ era. The document provides rigorous empirical evidence of correctness, statistical robustness, and optimization behavior using the H₂ VQE benchmark. Validation includes full Hamiltonian spectral equivalence (Qiskit Nature vs OpenFermion), shot-noise scaling analysis, multi-start optimization studies, and a comprehensive unit-test suite. This white paper is intended as a technical reference for researchers, industry partners, and decision-makers evaluating QCOS as a viable quantum software system rather than a performance-claim-driven.
Quantum computing research increasingly depends on complex software stacks, yet the reproducibility of published results does not receive the priority and longevity mandated by recommendations of large international scientific bodies and best practices in software-centric systems research. In this paper, we present a combined manual and automated large-scale analysis of the reproducibility landscape in quantum computing research, quantify shortcomings, and derive actionable steps forward.
We manually evaluate a curated sample of 127 papers using a five-question framework that covers code availability, environment specification, documentation, hardware description, and executability. To place these findings in a broader context, we conduct an automated large-scale screening of nearly 5000 quantum computing papers for the same reproducibility indicators. Our manual analysis reveals that only 24.4% of the sampled papers provide code artefacts, and among those, 64.5% fail to execute successfully in a clean environment. This assessment is corroborated by a large-scale automated analysis that yields a consistent code availability rate of 26.8%. Further, it shows that approximately one-third of the papers with accessible code lack machine-readable environment specifications.
The results in this paper indicate that reproducibility is not yet consistently achieved in quantum computing research. In response, we outline a set of practical recommendations that address the observed failure modes and illustrate how reproducibility can be improved in practice.