Quantum-control optimisation typically begins with calibration records assumed to remain applicable to the intended operation. Yet coherence, phase references, coupling, read-out response and subsystem interactions vary with time and operating context. Local calibration success may therefore be insuffi- cient to justify joint or changed-context control, allowing optimisation to pursue candidates founded on stale, incomplete or inapplicable evidence. We propose an evidence-governed admissibility framework that checks evidence role, scope, freshness, provenance and composition authority before constructing control candidates. Its computational layers have distinct functions: Layer 1 classifies routes as Permit, Withhold or Revalidate, retaining auditable native states and reasons; Layer 2A constructs and screens reduced-model candidates only for permitted routes; and Layer 2B checks applicable end-to-end workflows. Synthetic integration and authority-ablation tests demonstrate enforceable differences in permission, and the implementation passed 150/150 core and 10/10 slew-specific tests. These are computational findings, not hardware validation. We also provide two complementary prospective experimental arms for evaluating practical benefit. Arm 1 (predictive lift) freezes assessments before hardware outcomes are known and tests whether they predict the performance of independently authorised ordinary-baseline operations better than calibration- or availability-only benchmarks. Arm 2 (constructive recovery) tests whether initially unsuitable cases can yield authorised alternative controls after evidence refresh or reconfirmation, a new Layer 1 decision and downstream construction, while preserving the original classification. We release code, schemas, guided workbooks, fixtures and prespecified protocols for independent pilots. Hardware validation could establish whether this approach reduces wasted searches and improves control reliability or qubit performance.
Quantum-control optimisation typically begins with calibration records assumed to remain applicable to the intended operation. Yet coherence, phase references, coupling, read-out response and subsystem interactions vary with time and operating context. Local calibration success may therefore be insuffi- cient to justify joint or changed-context control, allowing optimisation to pursue candidates founded on stale, incomplete or inapplicable evidence. We propose an evidence-governed admissibility framework that checks evidence role, scope, freshness, provenance and composition authority before constructing control candidates. Its computational layers have distinct functions: Layer 1 classifies routes as Permit, Withhold or Revalidate, retaining auditable native states and reasons; Layer 2A constructs and screens reduced-model candidates only for permitted routes; and Layer 2B checks applicable end-to-end workflows. Synthetic integration and authority-ablation tests demonstrate enforceable differences in permission, and the implementation passed 150/150 core and 10/10 slew-specific tests. These are computational findings, not hardware validation. We also provide two complementary prospective experimental arms for evaluating practical benefit. Arm 1 (predictive lift) freezes assessments before hardware outcomes are known and tests whether they predict the performance of independently authorised ordinary-baseline operations better than calibration- or availability-only benchmarks. Arm 2 (constructive recovery) tests whether initially unsuitable cases can yield authorised alternative controls after evidence refresh or reconfirmation, a new Layer 1 decision and downstream construction, while preserving the original classification. We release code, schemas, guided workbooks, fixtures and prespecified protocols for independent pilots. Hardware validation could establish whether this approach reduces wasted searches and improves control reliability or qubit performance.
This record contains a manuscript and supporting reproducibility materials for “Ledger-Valid Recovery Diagnostics for Quantum Computing: A Prespecified Sequential Audit for Claim-Limited Recovery Validation.” The paper proposes a methodological validation framework for quantum-computing recovery claims. Its central claim is that endpoint recovery success is necessary but not sufficient for a strong recovery claim when calibration failure, false-carrier acceptance, postselection artifacts, leakage, sentinel blindness, or hidden discard can carry failure modes out of view. The manuscript introduces a prespecified S0–S4 ledger audit that converts familiar diagnostic ingredients—label calibration, candidate recovery, false-carrier challenge tests, postselection/leakage accounting, and sentinel observability—into an ordered, claim-limiting taxonomy. A supportive recovery claim is permitted only when all required gates pass; otherwise, the first failed stage determines a lower claim ceiling and an interpretable failure class. The evaluation is based on synthetic endpoint-level validation suites rather than circuit-level simulation or hardware data. These suites test the behavior of the decision architecture under clean support, degraded observability, false-carrier artifacts, postselection/leakage artifacts, sentinel-blind cases, correlated artifacts, and adversarial overlap. The results show a strong specificity/sensitivity tradeoff: the full ledger protocol sharply reduces synthetic non-support supportive-call rates relative to endpoint-only reporting, but with substantial loss of mechanism-positive support sensitivity. The paper explicitly treats this as a conservative claim-validity architecture, not as a new quantum error-correcting code, new physical observable, hardware speedup, or proof of improved device performance. The supporting materials document the final synthetic validation protocol, endpoint definitions, decision-bound implementation, direction-aware threshold sensitivity analysis, latent-threshold eligibility analysis, mechanism-negative/latent-eligible boundary cases, and figure/table traceability. The archive is intended to support review of the manuscript’s aggregate numerical claims and methodological provenance. Hardware implementation, independent simulator validation, and full raw-event-ledger reproduction are identified as future extensions. Suggested keywords: quantum computing; quantum error correction; validation methodology; recovery diagnostics; leakage; postselection; benchmarking; synthetic validation; decision architecture; reproducibility.
Willis (2026) argues that a recursively self-improving system may increase its effective quantum capability without acquiring additional hardware, and that the resulting hybrid classical-quantum execution path becomes progressively harder to characterise exhaustively. The proposed response abandons exhaustive reconstruction as an assurance objective and instead requires that specified properties hold before computation is permitted to affect consequential state, with five quantum execution controls layered beneath substrate-independent constitutional invariants. This paper accepts that architecture and contests two of its load-bearing predicates. The compilation equivalence predicate. GovernanceEquivalent(C(a), C(e)) carries the entire compilation-integrity argument and is left undefined. It cannot be read as semantic equivalence: deciding circuit equivalence is QMA-complete (Janzing, Wocjan & Beth, 2005), and remains so for the shallow circuits typical of transpiled output, while semantic sameness is in any case the wrong target given what transpilation exists to do. The predicate must therefore be evaluated relative to a declared set of governance-relevant properties. That set is itself a governance object, and under recursive self-improvement it is reachable through the toolchain. An attack follows in which a compilation change justified purely on performance grounds renders a governance-relevant property unmeasurable over the executed representation, so that the permit gate continues to evaluate correctly over a test that no longer discriminates. Traceability of the executed artefact to the authorised one does not prevent this, because traceability and property preservation come apart precisely when a transformation is faithful in derivation and lossy in representation. The sampling boundary. The authorised shot count is specified as a ceiling. A ceiling bounds resource consumption; it does not bound evidentiary resolution, which varies in the same direction. Admissibility evaluated on a point estimate is therefore unsound under an adversarial or merely cost-optimising choice of sample size, and error mitigation supplies a second route that widens the uncertainty region while appearing to improve result quality. Evaluating admissibility over a confidence region rather than a point estimate is sound, imposes the necessary floor implicitly, and fails closed without requiring a separately specified minimum. An authorised sequential-sampling rule is a second sound form, subject to the condition that the stopping rule be fixed prior to sampling. Composition. The two results are not independent. The repair proposed for the second introduces a parameter that is vulnerable to the first, and the properties governing sampling can themselves be attenuated out of the property set. Sound sampling governance presupposes governed property-set membership. General form. Both attacks share a shape: an invariant is enforceable only to the extent that the instrument deciding it — its property set, tolerance, confidence level, sample size, or measurability assumption — is unreachable from inside the system it constrains. Under recursive self-improvement there is always gradient in those parameters, because they trade against latency, cost and throughput, which are the objectives the recursion optimises. No adversarial disposition is required. The paper closes by applying its own criterion reflexively. A response whose complaint is that declarations are mistaken for proof cannot end with declarations and leave the matter there. A governance requirement is structural when its violation is observable without relying on the cooperation of the governed system, and declarative when it is not; three of the four requirements proposed here are organisational by that test, and one is not. This work continues the author's earlier treatment of trajectory-level governance and the Constitutional Failure Boundary methodology (Zenodo 10.5281/zenodo.18980403) and of admissibility operators and bounded reachability in probabilistic decision systems (Zenodo 10.5281/zenodo.20057043), extended here from classical to hybrid quantum execution. No implementation is described and none should be inferred. Attribution: the counterfactual authorisation test adapted in §3.6 was proposed by R. Muro. The Layer 3 / Layer 1 redefinition formulation in §3.5 and the authorised sequential-sampling rule in §4.4 are J. M. Willis's, offered in public response to an earlier and shorter statement of these arguments.
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.
As autonomous systems and AI agents begin to access quantum cloud resources, existing agent governance frameworks enforce only budgets and delegation scope, not physical execution quality. We present a governance framework that enforces hardware fidelity constraints before permitting agent-mediated quantum computation. The framework extends cryptographic delegation chains with physics facets (coherence times, gate errors, readout errors, calibration freshness) that participate in the same monotonic narrowing invariant as budget and scope constraints. A cross-backend experiment on IBM Quantum hardware (ibm_fez, ibm_marrakesh, ibm_kingston) shows that governance correctly denies a backend where qubit T1 falls below the delegation threshold (39.1 µs vs. 80 µs minimum), while permitting backends meeting the constraint. A counterfactual experiment indicates a 5.2 percentage point Bell state fidelity gap between the denied and permitted backends (92.9% vs. 98.1%), widening to 7.7 points on a 4-qubit GHZ circuit. Governance overhead is 4.2 ms for policy evaluation and receipt signing. Ed25519-signed receipts bind each governance decision to the hardware calibration state at the moment of evaluation.
As quantum computing continues to scale, quantum measurement and control (QMC) are increasingly constrained by calibration workflow complexity and by requirements for low-latency execution, robust exception handling, and traceable workflow governance. Existing frameworks for QMC are specialized and task-specific, while language-model-based agents for QMC suffer from excessive latency and cannot satisfy the strict timing and control-density demands of large-scale quantum systems. Here we propose QMClaw, a general, workflow-oriented framework for QMC built, featuring a local-first, tool-governed, robust architecture. At its core is a RuleEngine-centered control layer that processes structured context, performs rule-based state transitions, and generates execution plans for typical calibration workflows. Language models are used only for natural-language interaction, high-level task understanding, and exception support, keeping the critical fast path efficient. We implement a single qubit tune-up workflow as a demonstration and validation using real quantum device dataset. We also prove that the framework achieves quantitatively acceptable levels in terms of resource cost, LLM calling times and decision latency, enabling its practical deployment in large-scale quantum qubit measurement and control scenarios. This work presents a general workflow-oriented framework for QMC and provides evidence that rule-centered architectures are a promising design choice for scalable quantum-system calibration.