Introduction/Importance of Study: The application of quantum Artificial Intelligence (QAI) that combines artificial intelligence and quantum computing allows simulation, optimization, and decision-making capabilities never seen before, but introduces ethical and governance issues that are beyond conventional software engineering paradigms.Novelty statement: In this paper, we introduce the Responsible Quantum-AI Software Engineering (RQAI-SE) framework, a systematic integration of quantum-specific mechanisms for ethics, accountability, fairness, and transparency into the QAI software lifecycle.The framework comprises five hierarchically structured engineering layers, addresses four categories of quantum-specific ethical risks (algorithmic bias, lack of transparency, access inequality, andregulatory gaps), and explicitly maps its components to nine governance pillars established by the World Economic Forum (WEF) —structural contributions that distinguish RQAI-SE from existing QSE frameworks that treat ethics as secondary or post-development concerns.Material and Method: The study employs a conceptual design methodology grounded in a systematic review of 40 high-quality sources spanning quantum software engineering, AI ethics, and governance (2021–2025). The framework is validated through comparative analysis with existing QSE frameworks Table 1 and criterion-referenced mapping to the WEF nine governance pillars Table 3.Result and Discussion: The suggested RQAI-SE framework shows how quantum-AI systems can be designed to be both computationally efficientand responsible toward societyusing human-understandable quantum design, the ethical development cycle of the process, and governance models that are multidisciplinary. In contrast to the more performance-focused QSE strategies, the RQAI-SE directly deals with quantum-specific ethical risks, including but not limited to opacity, amplifying bias of probabilistic guarantees, and governance loopholes, and allows creating trustworthy, inclusive, and accountable. The model also offers a guided basis to align the emerging QAI technologies to the long-term societal values,as well as facilitating scalable and sustainable quantum software development.Concluding Remarks: RQAI-SE provides an effective and prospective basis of responsible QAI development, under which innovations in quantum intelligence are always within the ethical, social, and governance requirements. The framework identifies five priority areas for future research,including empirical case study validation and international certification standards.
Introduction/Importance of Study: The application of quantum Artificial Intelligence (QAI) that combines artificial intelligence and quantum computing allows simulation, optimization, and decision-making capabilities never seen before, but introduces ethical and governance issues that are beyond conventional software engineering paradigms.Novelty statement: In this paper, we introduce the Responsible Quantum-AI Software Engineering (RQAI-SE) framework, a systematic integration of quantum-specific mechanisms for ethics, accountability, fairness, and transparency into the QAI software lifecycle.The framework comprises five hierarchically structured engineering layers, addresses four categories of quantum-specific ethical risks (algorithmic bias, lack of transparency, access inequality, andregulatory gaps), and explicitly maps its components to nine governance pillars established by the World Economic Forum (WEF) —structural contributions that distinguish RQAI-SE from existing QSE frameworks that treat ethics as secondary or post-development concerns.Material and Method: The study employs a conceptual design methodology grounded in a systematic review of 40 high-quality sources spanning quantum software engineering, AI ethics, and governance (2021–2025). The framework is validated through comparative analysis with existing QSE frameworks Table 1 and criterion-referenced mapping to the WEF nine governance pillars Table 3.Result and Discussion: The suggested RQAI-SE framework shows how quantum-AI systems can be designed to be both computationally efficientand responsible toward societyusing human-understandable quantum design, the ethical development cycle of the process, and governance models that are multidisciplinary. In contrast to the more performance-focused QSE strategies, the RQAI-SE directly deals with quantum-specific ethical risks, including but not limited to opacity, amplifying bias of probabilistic guarantees, and governance loopholes, and allows creating trustworthy, inclusive, and accountable. The model also offers a guided basis to align the emerging QAI technologies to the long-term societal values,as well as facilitating scalable and sustainable quantum software development.Concluding Remarks: RQAI-SE provides an effective and prospective basis of responsible QAI development, under which innovations in quantum intelligence are always within the ethical, social, and governance requirements. The framework identifies five priority areas for future research,including empirical case study validation and international certification standards.
Abstract The convergence of Quantum Computing (QC), Quantum Software Engineering (QSE), and Artificial Intelligence (AI) presents transformative opportunities across various domains. However, existing methodologies inadequately address the ethical, security, and governance challenges arising from this technological shift. This paper highlights the urgent need for interdisciplinary collaboration to embed ethical principles into the development of Quantum AI (QAI) and QSE, ensuring transparency, inclusivity, and equitable global access. Without proactive governance, there is a risk of deepening digital inequalities and consolidating power among a select few. We call on the software engineering community to actively shape a future where responsible QSE and QAI are foundational for ethical, accountable, and socially beneficial technological progress.
The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) ushers in a transformative era of computational capability, with profound implications for science and industry alongside significant technical, ethical, and environmental challenges. This article explores quantum-enhanced AI (QAI), focusing on foundational quantum principles—superposition, entanglement, and quantum parallelism—and their applications in molecular simulation, financial optimization, and climate modeling. While quantum algorithms offer exponential speedups for Machine Learning (ML) and optimization, persistent barriers such as qubit decoherence, scalability limitations, and high energy demands hinder progress. Hybrid quantum-classical architectures are identified as critical transitional tools, combining quantum processors for specialized tasks like combinatorial optimization with classical systems for error correction and data preprocessing. Near-term commercial healthcare, finance, and logistics applications are highlighted, including pilot projects using quantum neural networks (QNNs) for drug discovery, portfolio risk analysis, and supply chain optimization. However, dual-use risks—such as quantum-powered surveillance and threats to classical encryption—emphasize the need for post-quantum cryptography (PQC) and ethical frameworks. Environmental concerns, particularly the carbon footprint of superconducting qubits, drive calls for sustainable innovations like photonic qubit designs and renewable-powered quantum data centers. Anticipated milestones include fault-tolerant QNNs, standardized benchmarks for quantum advantage, and democratized cloud access. Achieving these goals requires addressing interdisciplinary talent shortages, algorithmic biases, and global disparities in quantum infrastructure. This work advocates for governance models integrating sustainability metrics and equity-focused policies to steer QAI development responsibly. By harmonizing technical innovation with ethical foresight, the quantum-AI synergy can advance solutions to global challenges while mitigating misuse and ecological harm risks.
Quantum computing poses a severe threat to standard digital security protocols, leaving artificial intelligence (AI) data pipelines and decision-support systems highly exposed. As large-scale quantum attacks become computationally feasible, the public-key cryptography currently securing data transmission will likely fail. This vulnerability threatens sectors heavily reliant on AI—including finance, healthcare, and critical infrastructure escalating the probability of data breaches, integrity losses, and compliance failures. To address these interconnected security, governance, and ethical hurdles, this paper introduces the Integrated Quantum-Resilient AI Framework (IQRAI). IQRAI establishes a structured governance model combining proactive quantum risk assessments, post-quantum cryptography (PQC), and continuous security monitoring. A core focus of the framework is balancing cryptographic migration with strict ethical requirements, specifically data privacy, algorithmic transparency, fairness, and auditability. Furthermore, the study reviews ongoing global standardization efforts for PQC transitions to define baseline requirements for sustained system resilience. By comparing existing mitigation models across metrics of operational friction, implementation difficulty, and security yield, the research identifies a critical operational gap. The analysis demonstrates that merely swapping cryptographic algorithms is insufficient for achieving quantum-resilient AI; securing these systems demands a unified approach that links security engineering, ethical oversight, and regulatory adherence. Ultimately, this work delivers a strategic roadmap for organizations to systematically harden their AI ecosystems against quantum threats while maintaining operational trust and legal compliance.
This paper proposes a novel conceptual framework that integrates the advanced capabilities of quantum computing to address the urgent need for responsible and inclusive Artificial Intelligence (AI) development. It reviews current challenges in AI, such as bias, lack of inclusivity, and the computational limitations faced by classical computing methods in solving complex societal problems. By harnessing quantum computing, this framework aims to overcome these barriers, enabling faster, more efficient AI solutions that are ethically grounded and universally accessible. By adopting a holistic approach that integrates technical innovation with ethical considerations and stakeholder engagement, we believe that quantum computing can serve as a catalyst for the development of AI technologies that are not only more advanced but also more inclusive, responsible, and beneficial for society as a whole. This concept paper serves as a foundational framework for further research, collaboration, and action in the intersection of quantum computing and AI, with the ultimate goal of harnessing the transformative potential of these technologies to address pressing societal challenges and promote human well-being. Keywords: Quantum Computing, AI, Development, Responsible.
While quantum technologies move out of laboratory research toward deployment, there are many examples of how post-quantum cryptography (PQC) migration and quantum networking can be considered ‘near-term’ governance stress tests. While this is happening, artificial intelligence is being increasingly applied in the design, calibration, and control of quantum devices and networks, which creates ‘quantum agent systems’ where agentic decision components interact with quantum computing, communication, and sensing capabilities. These two trajectories intensify ethical and societal concerns such as privacy and surveillance, cryptographic disruption, compound opacity and explainability limitations, bias and unequal benefit distribution, and dual-use and geopolitical escalation risks. This chapter brings together the provided quantum governance, quantum ethics, societal research, legal and AI governance literature to translate principles into practice. This chapter contributes (i) a governance target definition of quantum agent systems; (ii) a critical comparison of national, international, and organizational governance instruments; and (iii) a structured ESG assurance framework that links risks to stakeholders, governance mechanisms, assurance metrics, and expected outcomes. The chapter also identifies methodological limits and evidence gaps and highlights the need for early intervention, capacity-building, and intelligibility to enable inclusive oversight while technologies remain malleable. The contributions of this chapter provide a roadmap for responsible governance of quantum agent systems under uncertainty and contested trade-offs.
In this paper, we conduct a systematic literature review of (semi-) automated approaches to Quantum Software Engineering (QSE) and Quantum Artificial Intelligence (QAI). Prior work in the literature indicated that both Software Engineering (SE) and Artificial Intelligence (AI) practices may become more efficient by using (semi-) automated approaches. This also holds in the Quantum Computing (QC), Quantum Information Science (QIS), and Quantum Engineering (QE) world, as well as in hybrid quantum-classical applications. In fact, automation is even more crucial in such cases since there is a limited number of developers and AI experts (e.g., data scientists) who possess the required knowledge and skills in QC. Moreover, in hybrid setups, automation may help decide what part of the application should be deployed on quantum hardware and on which of the available quantum platforms, if applicable. This can be a significant help to achieve productivity leap and efficiency even for subject matter experts. Unlike prior literature reviews and surveys, this work focuses on automation in SE and AI for quantum and hybrid quantum-classical applications and identifies the recent trends and future directions through a systematic literature review. We are interested in methods and techniques that can enable a broader development and deployment of quantum and hybrid AI-enabled software systems.