发表机构
Universidad Internacional de la Rioja UNIR; Nantes Université; École Centrale Nantes; CNRS; National Institute of Informatics; Universidad de Oviedo（国际拉里奥哈大学（UNIR）; 南特大学; 南特中央理工学院; 法国国家科学研究中心; 情报学研究所; 奥维耶多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出通用框架QXymb及其实例QILP-0，通过观测量子电路行为构建其观测声明式孪生体，在两类QML实验中实现了完全无冲突的精确重构。
AI 中文摘要
本文介绍了QXymb，这是一个用于构建量子电路观测声明式孪生体的通用框架，并开发了其首个完整的0阶特化版本QILP-0。QILP-0会在声明的观测范围内，根据观测到的电路行为构建一个有限多值命题逻辑程序。该流程会依据可复现的结构分级和声明的观测参考视界，逐步遍历声明的量子可观测变量族。进度通过与固定的、不依赖目标的参考相对的参考相关覆盖度来量化。可观测变量的响应通过不依赖目标的几何结构进行组织，而保留的潜在结构会在符号处理前确定性地映射回原始可观测变量列，以保留观测语义和来源。选定的可观测变量剖面通过可允许的、不依赖目标的离散化转换为有限关系。目标仅在之后用于审计孪生体可允许性并导出声明式理论。当一个理论能完全且正确地重构所得的有限任务条件离散关系时，它被认证为精确的观测声明式孪生体。因此，逻辑精确性与数值、后端、提供者及离散化不确定性相分离，后者作为审计元数据保留。验证使用两种互补的量子机器学习（QML）设置：详尽的条纹与条形（Bars & Stripes）实验比较了16到100量子比特的乘积嵌入和网格-CZ（grid-CZ）嵌入，并应用了原生离散分支；低深度MNIST分析在训练后的变分量子变换前后的全部14708个0/1数字实例，并应用了连续离散化。在所有报告的关系中，导出的QILP-0理论均实现了完整、无冲突的重构，严格准确率等于1。
英文摘要
This paper introduces QXymb, a general framework for constructing observational declarative twins of quantum circuits, and develops QILP-0, its first complete order-0 specialization. QILP-0 constructs a finite multi-valued propositional logic program from observed circuit behaviour within a declared observational scope. The pipeline traverses a declared family of quantum observables incrementally according to a reproducible structural grading and a declared observational reference horizon. Progress is quantified through reference-relative coverage against a fixed target-independent reference. Observable responses are organized through target-independent geometry, while retained latent structure is mapped deterministically back to original observable columns before symbolic processing, preserving observational semantics and provenance. Selected observable profiles are converted into a finite relation through admissible target-independent discretization. The target is used only afterwards to audit twin-admissibility and induce the declarative theory. A theory is certified as an exact observational declarative twin when it completely and correctly reconstructs the resulting finite task-conditioned discrete relation. Logical exactness is therefore separated from numerical, backend, provider, and discretization uncertainty, which is retained as audit metadata. Validation uses two complementary QML settings. Exhaustive Bars & Stripes experiments compare product and grid-CZ embeddings from 16 to 100 qubits and exercise the native-discrete branch. Low-Depth MNIST analyses all 14,708 digit-0/1 instances before and after a trained variational quantum transformation and exercises continuous discretization. In every reported relation, the induced QILP-0 theory achieves complete, conflict-free reconstruction with strict accuracy equal to one.
Comments39 pages, 7 figures. Submitted to Knowledge-Based Systems
