This paper proposes a conceptual framework for coordinating quantum computation, clas-sical control, and artificial-intelligence-assisted development. The central design principle isto separate suggestions from execution authority: an AI assistant may propose circuits or ex-plain results, while deterministic software validates executable artifacts, enforces resource limits,and records experimental provenance. We formulate a policy-constrained quantum-job contractand illustrate its scientific component through an analytically solvable single-qubit variationalproblem. The example distinguishes exact expectation values from finite-shot estimates andshows why software authorization alone cannot establish physical accuracy. Drawing on workon enterprise AI responsibility allocation, runtime defense, and modular software generation,the framework adapts architectural ideas to quantum research without treating those sourcesas evidence of quantum performance. We provide an evaluation protocol covering scientific ac-curacy, policy enforcement, reproducibility, and end-to-end cost. This is a design proposal withan analytical illustration, not an experimental report; it makes no claim of measured reliabilitygains or quantum advantage.
This paper proposes a conceptual framework for coordinating quantum computation, clas-sical control, and artificial-intelligence-assisted development. The central design principle isto separate suggestions from execution authority: an AI assistant may propose circuits or ex-plain results, while deterministic software validates executable artifacts, enforces resource limits,and records experimental provenance. We formulate a policy-constrained quantum-job contractand illustrate its scientific component through an analytically solvable single-qubit variationalproblem. The example distinguishes exact expectation values from finite-shot estimates andshows why software authorization alone cannot establish physical accuracy. Drawing on workon enterprise AI responsibility allocation, runtime defense, and modular software generation,the framework adapts architectural ideas to quantum research without treating those sourcesas evidence of quantum performance. We provide an evaluation protocol covering scientific ac-curacy, policy enforcement, reproducibility, and end-to-end cost. This is a design proposal withan analytical illustration, not an experimental report; it makes no claim of measured reliabilitygains or quantum advantage.
AI-assisted quantum programming requires a clear separation between generating candidate solutions and authorizing their execution. This paper presents a conceptual framework that assigns proposal generation to an AI assistant, validation and orchestration to a classical controller, and measurement execution to a quantum backend. A structured quantum-job contract links circuit artifacts, observables, shot allocations, backend selection, and provenance to explicit admission checks. The framework adapts responsibility-allocation and runtime-control principles from enterprise AI architectures while distinguishing software compliance from scientific correctness. An analytically solvable single-qubit variational example illustrates expectation-value estimation, finite-shot uncertainty, and resource-aware measurement allocation. The analysis shows why an authorized execution must still be assessed for measurement consistency, statistical uncertainty, and physical bias. A proposed evaluation protocol addresses accuracy, policy enforcement, reproducibility, operational cost, and researcher workload. The contribution is an architectural design and analytical illustration rather than an experimentally validated system; no measured reliability improvement or quantum advantage is claimed.
Connecting AI-generated quantum programs to executable experiments requires more than syntactically valid code: the workflow must also control execution authority and preserve the basis for interpreting its results. This paper develops a conceptual architecture organized around three responsibilities: AI-assisted circuit proposal, deterministic classical validation, and quantum measurement execution. A quantum-job contract binds circuit artifacts, measurement settings, resource allocations, backend information, and provenance to explicit admission conditions. The design separates permission to run an experiment from evidence that its output answers the intended scientific question. An analytical single-qubit variational example demonstrates this distinction through exact energy calculations, finite-shot variance, and budget-constrained measurement allocation. Responsibility allocation, runtime interception, and staged transformation provide the architectural principles, while uncertainty reporting and reference calculations supply the scientific checks. A prospective evaluation protocol covers accuracy, policy violations, reproducibility, end-to-end cost, and researcher effort. The framework is presented as a design proposal supported by mathematical illustration, not as a tested implementation; its operational benefits and applicability to larger quantum workloads remain to be established.
Existing artificial intelligence governance and runtime execution governance frameworks were developed primarily within classical computing environments. While autonomous and agentic systems have introduced new governance challenges, most governance architectures continue to rely upon assumptions regarding deterministic execution, reproducibility, authorization validity, and auditability. Quantum and hybrid classical-quantum systems introduce execution characteristics that challenge these assumptions. Probabilistic measurement, circuit compilation and transpilation, hardware-dependent execution behavior, calibration drift, shot-based execution, hybrid computational workflows, and emerging distributed quantum architectures create governance obligations not fully addressed by existing execution-governance models. This paper proposes an extension of execution-layer governance into quantum environments through the introduction of Quantum Runtime Governance Invariants and corresponding Quantum Governance Requirements. The proposed invariants identify governance properties that must remain true regardless of implementation details, hardware platforms, programming models, or execution environments. These invariants are explicitly tied to governance properties including authorization integrity, admissibility integrity, provenance continuity, governance evidence integrity, replayability, governance continuity, and governance authority. The paper further introduces the concept of Probabilistic Admissibility, in which governance systems evaluate and authorize bounded outcome distributions rather than deterministic outcomes. Building upon this foundation, the paper derives a structured set of Quantum Governance Requirements spanning quantum resource governance, circuit governance, compilation governance, probabilistic outcome governance, shot governance, calibration governance, hybrid classical-quantum governance, quantum artificial intelligence governance, and distributed quantum governance. Finally, the paper examines the implications of these concepts for the Artificial Intelligence Governance Control Plane (AGCP), arguing that quantum systems do not require a fundamentally new governance architecture. Rather, they require an extension of existing execution-governance semantics capable of operating over probabilistic execution environments. The resulting framework establishes an initial foundation for AGCP-Quantum profiles, governance conformance assessment methodologies, registry assessment frameworks, governance assurance models, and future standards development activities.
Objective: To synthesize the state of the art on how quantum computing (QC) and artificial intelligence (AI) intersect, including algorithmic foundations, software/hardware stacks, empirical evidence of advantage, applications, and open challenges. Methods: We executed a structured search (2019 – 20th August 2025) across major journals (Nature portfolio, APS/PR, Elsevier), arXiv, and official framework documentation (Qiskit, Cirq/TFQ, PennyLane; AWS Braket; NVIDIA CUDA-Q). We included peer‑reviewed studies, comprehensive surveys, and official docs; we excluded opinion pieces and non-reproducible claims. Results: The field converges on hybrid quantum‑classical workflows with two dominant AI families: (i) quantum kernel methods and (ii) variational circuits/QNNs. Trainability remains a core challenge (barren plateaus and noise), with mitigation strategies advancing (local costs, layerwise training, engineered dissipation, learned decoders). Frameworks and cloud orchestrators now support end‑to‑end hybrid training. Empirical “utility” demonstrations in physics‑inspired tasks exist, while domain‑level advantage in mainstream ML is unproven and data‑dependent. Conclusions: QC‑AI integration is maturing into reproducible hybrid stacks. Near‑term value lies in quantum kernels with tailored feature maps, physics‑regularized QNNs, simulator‑accelerated pipelines, and AI‑for‑QC (calibration/decoding). Broad ML advantage awaits lower‑noise hardware and quantum‑friendly data embeddings.
Artificial intelligence (AI) infrastructure is becoming increasingly constrained by electricity demand, heat generation, cooling requirements, water consumption, and the lifecycle impacts of specialized hardware. This article proposes a conceptual framework for Quantum AI Engineering for Sustainable Intelligence that treats energy, information, computation, communication, cooling, and water consequences as coupled system-design variables. The framework is informed by a structured narrative synthesis spanning sustainable AI, data-center energy and water use, photonic computing, energy harvesting, quantum sensing, quantum communication, quantum batteries, and hybrid computing. Its central theoretical contribution is the Quantum Energy-Information Interface (QEII): a future systems abstraction that connects observable physical-resource states to computation and communication decisions across classical, photonic, and, where justified, quantum components. The revised framework formalizes cross-layer energy and information flows, introduces state and control variables and a multi-objective sustainability function, separates technologies into deployable, emerging, and exploratory readiness categories, and provides a comparative validation protocol for classical, photonic, quantum-assisted, and hybrid architectures. Water sustainability is treated explicitly through direct cooling water, electricity-related water, embodied water, workload placement, and energy-water trade-offs. The framework does not assume quantum advantage or claim that quantum technologies create energy, eliminate electricity, or enable faster-than-light communication. Instead, it defines testable conditions under which a new component should be considered sustainable: measurable system-level improvement relative to an optimized classical baseline while maintaining accuracy, latency, reliability, security, cost, and lifecycle constraints. The resulting research agenda is intended to support reproducible simulation, prototype development, and eventually system-level evaluation of energy- and water-aware AI infrastructure.
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.