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.
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.
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.
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.
Incomplete access to quantum states does not imply that a hybrid quantum–AI system cannot be controlled. Physical isolation, restricted interfaces and independent enforcement can limit a system's effects without reconstructing its complete internal state. Quantum information theory nevertheless matters for the evidence available to auditors, particularly when an execution consumes an unknown quantum input that cannot be prepared again. This conceptual paper separates four assurance objectives: state reconstruction, verification of computation, behavioural evaluation and boundary control. It then distinguishes repeatable state preparation from access to a single, unreproducible quantum input. The distinction identifies when repeated experiments, targeted measurements and verification protocols may support an audit, and when retrospective reconstruction cannot substitute for evidence collected during execution. A hypothetical hybrid classifier illustrates how execution records, statistical tests and an independent action policy address different claims. The paper proposes an evidence plan that specifies the access model, the property to be assessed and the assumptions supporting each assurance statement. It offers neither a new physical principle nor a general impossibility result for containment. Its narrower argument is that governance should plan for the evidence an architecture can actually preserve, rather than treating complete state access as either necessary for control or available on demand after an incident.
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.
The integration of large language models (LLMs) into scientific research is accelerating the realization of autonomous ``AI Scientists.'' While recent advancements have empowered AI to formulate hypotheses and design experiments, a critical gap remains in the execution of these tasks, particularly in the domain of quantum computing (QC). Executing quantum algorithms requires not only generating code but also managing complex computational resources such as QPUs and high-performance computing (HPC) clusters. In this paper, we propose an AI-driven framework specifically designed to bridge this execution gap through the implementation of a Model Context Protocol (MCP) server. Our system enables an LLM agent to process natural language prompts submitted as part of a job, autonomously executing quantum computing workflows by invoking our tools via the MCP. We demonstrate the framework's capability by performing essential quantum algorithmic primitives, including sampling and computation of expectation values. Key technical contributions include the development of an MCP server for quantum execution, a pipeline for interpreting OpenQASM code, an automated workflow with CUDA-Q for the ABCI-Q hybrid platform, and an asynchronous execution pipeline for remote quantum hardware using the Quantinuum emulator via CUDA-Q. This work validates that AI agents can effectively abstract the complexities of hardware interaction through an MCP-based architecture, thereby facilitating the automation of practical quantum research.