Predicate-based computation over state spaces separates a problem specification from the backend that realizes it. Building on the model introduced in arXiv:2606.15027, this paper studies QDSV as a semantic, multi-backend execution framework for quantum-oriented computation. We describe how QDSV, QIntent, and Qruba connect declarative problem intent to a structured semantic representation, realize that representation under heterogeneous backend constraints, and report execution trace outputs that separate model-level semantic outputs from backend-specific observations. The framework supports execution modes that do not require the original problem to be authored as a circuit, while still allowing circuit-compatible artifacts when required. As a case study, we evaluate EEG ictal/interictal classification using prepared signal features from the Bonn and Delhi datasets. The study compares classical machine-learning references, a circuit-first variational quantum classifier baseline, QDSV simulator executions, and controlled IBM Quantum hardware runs. The paper does not claim general quantum advantage or superiority over classical machine learning. Its contribution is a semantic execution validation showing how a problem-first representation can remain stable across simulator and hardware realizations while retaining interpretable execution trace outputs.
Predicate-based computation over state spaces separates a problem specification from the backend that realizes it. Building on the model introduced in arXiv:2606.15027, this paper studies QDSV as a semantic, multi-backend execution framework for quantum-oriented computation. We describe how QDSV, QIntent, and Qruba connect declarative problem intent to a structured semantic representation, realize that representation under heterogeneous backend constraints, and report execution trace outputs that separate model-level semantic outputs from backend-specific observations. The framework supports execution modes that do not require the original problem to be authored as a circuit, while still allowing circuit-compatible artifacts when required. As a case study, we evaluate EEG ictal/interictal classification using prepared signal features from the Bonn and Delhi datasets. The study compares classical machine-learning references, a circuit-first variational quantum classifier baseline, QDSV simulator executions, and controlled IBM Quantum hardware runs. The paper does not claim general quantum advantage or superiority over classical machine learning. Its contribution is a semantic execution validation showing how a problem-first representation can remain stable across simulator and hardware realizations while retaining interpretable execution trace outputs.
Quantum programs are difficult to validate: circuits are typically expressed as imperative gate sequences with limited inspectability, execution modalities must be selected manually, and outputs are inherently probabilistic. These challenges are compounded when programs must be portable across backend frameworks with differing internal conventions. We present a quantum domain-specific language (QDSL) that addresses these problems through three mechanisms: (i) intent-execution separation, where algorithmic constructs such as preparation, superposition, entanglement, and measurement are represented as inspectable objects compiled into backend-specific circuits only after pre-execution Intermediate Representation (IR) validation; (ii) first-class IR introspection, exposing circuit width, wire mapping, operation order, and measurement intent for developer inspection prior to execution; and (iii) assertion-guided modality inference, where the engine examines declared verification properties to automatically select sampling, statevector, or dual execution without user intervention. Results are logged in structured form to support reproducible regression testing. We evaluate our prototype on benchmark circuits spanning entanglement, oracle-based, structured-transform, and variational examples. In fault-injection experiments on Bell and 3-qubit GHZ circuits, a total-variation-distance detector identifies the injected faults across all evaluated shot budgets (True Positive Rate 1.0), with observed false positives (False Positive Rate 2.7-3.8%) only at 128 shots. Differential testing demonstrates numerical agreement across PennyLane and Qiskit compilation targets for the evaluated constructs after endianness canonicalization. IR generation costs are sub-millisecond.
Distributed quantum protocols require measurement outcomes produced at one node to control operations at another. We present QQLink, a programming and orchestration layer that expresses these dependencies in a single program, using workflow nodes, deferred measurement references, and declared entanglement, and derives the cross-node coordination automatically. Its planner constructs a provably acyclic dependency graph in time linear in the total size of the instructions' qubit footprints. The same programs execute through simulator backends or are lowered to dynamic circuits on partitioned quantum hardware. We evaluate seven protocol workflows. Exhaustive feedforward-branch tests on the simulator runtime path, over tomographically complete input sets, yield output-state infidelities of at most 5×10−12 and no incorrect classical outputs, and replaying every plan in random topological orders leaves each final state unchanged. Separate executor processes run the plans with delayed messages, with the joint quantum state held by a centralized simulation service. Every run with the full plan is correct, and removing required cross-node dependencies causes protocol failures. The lowered circuits match hand-written implementations of the same construction in two-qubit gate and conditional-block counts. Runs on two IBM Heron processors, each partitioned into virtual QPUs, demonstrate same-chip execution in two sessions a week apart. The runs also show large variation between sessions and losses beyond what calibration-derived noise models predict. Coherence-sensitive checks do not establish coherent Toffoli operation or preservation of the intended repetition-code superposition. These results support QQLink's workflow semantics on the tested execution paths and identify limits of the current hardware realization; execution across physically networked QPUs remains future work.
Variational quantum algorithms are hybrid quantum-classical workflows whose results depend on many interacting choices, including the ansatz, Hamiltonian, optimizer, backend, shot count, noise model, mitigation method, random seed, stopping criteria, and software versions. In current practice, this information is often scattered across code, configuration files, logs, backend metadata, and paper descriptions, making executions difficult to reproduce, compare, debug, and reuse. This paper proposes an ontology-supported framework for representing Variational Quantum Algorithm (VQA) execution records as structured and machine-readable software engineering artifacts. The framework defines a Web Ontology Language (OWL) ontology for modeling the main entities involved in VQA experimentation, including algorithms, circuits, ansatzes, Hamiltonians, optimizers, backends, noise models, mitigation techniques, execution steps, software environments, measurement outcomes, and results. It further combines the ontology with Shapes Constraint Language (SHACL) constraints for validating completeness and consistency, and SPARQL Protocol and RDF Query Language (SPARQL) competency queries for retrieving reproducibility-relevant information. We demonstrate the approach using Variational Quantum Eigensolver (VQE) execution records, including a valid record and intentionally incomplete or inconsistent examples. The results show that the framework can represent complete VQA execution contexts, detect missing or malformed metadata, and support query-based inspection of information needed for reproducible quantum software experimentation.
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.
Progress in quantum computing and networking depends on codesign across abstraction layers: device-level noise and heterogeneous hardware, algorithmic structure, and distributed classical control. We present QuantumSavory, an open-source toolkit built to make such end-to-end studies practical by cleanly separating a symbolic computer-algebra frontend from interchangeable numerical simulation backends. States, operations, measurements, and protocol logic are expressed in a backend-agnostic symbolic language; the same model can be executed across multiple backends (e.g., stabilizer, wavefunction, phase-space), enabling rapid exploration of accuracy-performance tradeoffs without rewriting the model. Furthermore, new custom backends can be added via a small, well-defined interface that immediately reuses existing models and protocols. QuantumSavory also addresses the classical-quantum interaction inherent to LOCC protocols via discrete-event execution and a tag/query system for coordination. Tags attach structured classical metadata to quantum registers and message buffers, and queries retrieve, filter, or wait on matching metadata by wildcards or arbitrary predicates. This yields a data-driven control plane where protocol components coordinate by publishing and consuming semantic facts (e.g., resource availability, pairing relationships, protocol outcomes) rather than by maintaining rigid object graphs or bespoke message plumbing, improving composability and reuse as models grow. Our toolkit is also not limited to qubits and Bell pairs; rather, any networking dynamics of any quantum system under any type of multipartite entanglement can be tackled. Lastly, QuantumSavory ships reusable libraries of standard states, circuits, and protocol building blocks with consistent interfaces, enabling full-stack examples to be assembled, modified, and compared with minimal glue code.
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.