mlsanityData to 14 Oct 2026

QILP-0: Constructing Observational Declarative Twins of Quantum Circuits

arXiv 2609.01049 ↗First posted 1 Sep 2026cs.AI quant-phcited by 0

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Abstract (arXiv)

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.

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References · 12

  1. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  2. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
  3. 2014A Tutorial on Principal Component Analysiscited by 141
  4. 2018Differentiable Learning of Quantum Circuit Born Machinecited by 93
  5. 2023Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learningcited by 17
  6. 2020Operationally meaningful representations of physical systems in neural networkscited by 16
  7. 2024Opportunities and limitations of explaining quantum machine learningcited by 7
  8. 2025Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulatorcited by 5
  9. 2026Discovering quantum phenomena with Interpretable Machine Learningcited by 3
  10. 2026Disentanglement by means of action-induced representationscited by 2
  11. 2021LQP: The Dynamic Logic of Quantum Informationcited by 1
  12. 2022symQV: Automated Symbolic Verification of Quantum Programscited by 1

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