Self-Fibre: A Finite Quantum Reconstruction Model with Discrete Reconstruction Obstructions

Preprint; submitted to Journal of Mathematical Physics. We introduce Self-Fibre, a finite-dimensional mathematical framework in which relational structures between local quantum systems (“fibres”) are not postulated but selected from local fibre data through a variational reconstruction principle. Given a finite collection of local Hilbert-space fibre states, we define a reconstruction functional over candidate relational graphs balancing informational compatibility, pairwise reconstruction consistency, and structural complexity. We prove that this functional attains a minimum on the finite candidate space, and that the minimizer is unique under a reconstruction-gap condition. For finite systems of pure qubit fibres, we explicitly construct induced fibre comparison maps. For every reconstructible pair of fibre states, these maps are the unique minimal Hilbert–Schmidt-distance unitaries consistent with the pair. We further prove that the associated closedcycle holonomy satisfies an exact discrete identity expressing the global reconstruction obstruction as a sum of local composition defects transported around the cycle. This obstruction quantifies the failure of pairwise-consistent local reconstructions to form a globally consistent comparison structure. All results are established within the finite pure-qubit setting. We do not assume or derive a spacetime manifold, metric structure, or continuum limit. The reconstruction obstruction is proposed only as a candidate discrete precursor to geometric curvature, with any continuum correspondence left as an open problem.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-24
DOI
https://doi.org/10.5281/zenodo.22074989
Primary Topic
Quantum chaos and dynamical systems
Type
preprint
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preprint

Self-Fibre: A Finite Quantum Reconstruction Model with Discrete Reconstruction Obstructions

Qian Miao
Zenodo (CERN European Organization for Nuclear Research)
Quantum chaos and dynamical systems
preprint

Self-Fibre: A Finite Quantum Reconstruction Model with Discrete Reconstruction Obstructions

Qian Miao
preprint en

Abstract

Preprint; submitted to Journal of Mathematical Physics. We introduce Self-Fibre, a finite-dimensional mathematical framework in which relational structures between local quantum systems (“fibres”) are not postulated but selected from local fibre data through a variational reconstruction principle. Given a finite collection of local Hilbert-space fibre states, we define a reconstruction functional over candidate relational graphs balancing informational compatibility, pairwise reconstruction consistency, and structural complexity. We prove that this functional attains a minimum on the finite candidate space, and that the minimizer is unique under a reconstruction-gap condition. For finite systems of pure qubit fibres, we explicitly construct induced fibre comparison maps. For every reconstructible pair of fibre states, these maps are the unique minimal Hilbert–Schmidt-distance unitaries consistent with the pair. We further prove that the associated closedcycle holonomy satisfies an exact discrete identity expressing the global reconstruction obstruction as a sum of local composition defects transported around the cycle. This obstruction quantifies the failure of pairwise-consistent local reconstructions to form a globally consistent comparison structure. All results are established within the finite pure-qubit setting. We do not assume or derive a spacetime manifold, metric structure, or continuum limit. The reconstruction obstruction is proposed only as a candidate discrete precursor to geometric curvature, with any continuum correspondence left as an open problem.

Zenodo (CERN European Organization for Nuclear Research)
Quantum chaos and dynamical systems
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Self-Fibre: A Finite Quantum Reconstruction Model with Discrete Reconstruction Obstructions — Qian Miao · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS