The convergence of large language models (LLMs) and quantum computing has the potential to revolutionize software development for quantum optimization tasks. AI-assisted code generation, powered by models like OpenAI Codex, can accelerate the design of quantum algorithms by automating routine coding tasks and democratizing access to quantum programming. However, this innovation introduces a web of ethical, legal, and technical challenges. This paper investigates the implications of using LLMs to generate quantum code, focusing on intellectual property (IP) concerns, the risk of unintended outcomes, legal ambiguity, and dual-use scenarios. We propose an ethical architecture for responsible AI-assisted development, incorporating human-in-the-loop systems, license-compliance mechanisms, and auditing tools. Case studies illustrate potential failures in code correctness, security, and attribution. We conclude with recommendations for explainable AI systems, curated datasets, and governance models that ensure innovation without sacrificing safety or compliance. By addressing these concerns proactively, the community can guide LLM-powered quantum development toward a responsible future.
The convergence of large language models (LLMs) and quantum computing has the potential to revolutionize software development for quantum optimization tasks. AI-assisted code generation, powered by models like OpenAI Codex, can accelerate the design of quantum algorithms by automating routine coding tasks and democratizing access to quantum programming. However, this innovation introduces a web of ethical, legal, and technical challenges. This paper investigates the implications of using LLMs to generate quantum code, focusing on intellectual property (IP) concerns, the risk of unintended outcomes, legal ambiguity, and dual-use scenarios. We propose an ethical architecture for responsible AI-assisted development, incorporating human-in-the-loop systems, license-compliance mechanisms, and auditing tools. Case studies illustrate potential failures in code correctness, security, and attribution. We conclude with recommendations for explainable AI systems, curated datasets, and governance models that ensure innovation without sacrificing safety or compliance. By addressing these concerns proactively, the community can guide LLM-powered quantum development toward a responsible future.
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.
Code Large Language Models (Code LLMs) have emerged as powerful tools, revolutionizing the software development landscape by automating the coding process and reducing time and effort required to build applications. This paper focuses on training Code LLMs to specialize in the field of quantum computing. We begin by discussing the unique needs of quantum computing programming, which differ significantly from classical programming approaches or languages. A Code LLM specializing in quantum computing requires a foundational understanding of quantum computing and quantum information theory. However, the scarcity of available quantum code examples and the rapidly evolving field, which necessitates continuous dataset updates, present significant challenges. Moreover, we discuss our work on training Code LLMs to produce high-quality quantum code using the Qiskit library. This work includes an examination of the various aspects of the LLMs used for training and the specific training conditions, as well as the results obtained with our current models. To evaluate our models, we have developed a custom benchmark, similar to HumanEval, which includes a set of tests specifically designed for the field of quantum computing programming using Qiskit. Our findings indicate that our model outperforms existing state-of-the-art models in quantum computing tasks. We also provide examples of code suggestions, comparing our model to other relevant code LLMs. Finally, we introduce a discussion on the potential benefits of Code LLMs for quantum computing computational scientists, researchers, and practitioners. We also explore various features and future work that could be relevant in this context.
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.
Large Language Models (LLMs) have already reshaped many aspects of traditional software engineering, from code generation and debugging to automated testing and maintenance. As an important research sub-domain in the software engineering area, quantum software engineering (QSE) is poised to undergo a similar transformation. As QSE moves toward broader adoption, including the generation of quantum code, program testing, and automated program repair, LLMs will likely play a key supporting role in its development. However, despite encouraging early attempts, the community still lacks a roadmap for using LLMs in QSE. Without such a roadmap, opportunities may be missed, challenges may remain unrevealed, and risks may be overlooked. In this article, we present our vision for how LLMs can advance QSE, focusing on four core tasks: program modeling and analysis, program generation, testing, and repair. We outline the opportunities and unique challenges, identify the risks inherent in this integration, and propose a research agenda to guide the community toward realizing the full potential of LLM-powered QSE.
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.
Quantum programs are typically developed using quantum Software Development Kits (SDKs). The rapid advancement of quantum computing necessitates new tools to streamline this development process, and one such tool could be Generative Artificial intelligence (GenAI). In this study, we introduce and use the Qiskit HumanEval dataset, a hand-curated collection of tasks designed to benchmark the ability of Large Language Models (LLMs) to produce quantum code using Qiskit – a quantum SDK. This dataset consists of more than 100 quantum computing tasks, each accompanied by a prompt, a canonical solution, a comprehensive test case, and a difficulty scale to evaluate the correctness of the generated solutions. We systematically assess the performance of a set of LLMs against the Qiskit HumanEval dataset's tasks and focus on the models ability in producing executable quantum code. Our findings not only demonstrate the feasibility of using LLMs for generating quantum code but also establish a new benchmark for ongoing advancements in the field and encourage further exploration and development of GenAI-driven tools for quantum code generation.