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Asymptotic Expansions for Neural Network Approximations of Quantum Channels

arXiv Quantum Archived Mar 20, 2026 ✓ Full text saved

arXiv:2603.18033v1 Announce Type: new Abstract: This paper establishes the Quantum Voronovskaya--Damasclin (QVD) Theorem, providing a complete asymptotic characterization of Quantum Neural Network Operators in the approximation of arbitrary quantum channels. The result extends the classical Voronovskaya theorem from scalar approximation to the non-commutative operator framework of quantum information theory. We introduce rigorous quantum analogues of Sobolev and H\"older spaces defined through F

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    Quantum Physics [Submitted on 9 Mar 2026] Asymptotic Expansions for Neural Network Approximations of Quantum Channels Rômulo Damasclin Chaves dos Santos This paper establishes the Quantum Voronovskaya--Damasclin (QVD) Theorem, providing a complete asymptotic characterization of Quantum Neural Network Operators in the approximation of arbitrary quantum channels. The result extends the classical Voronovskaya theorem from scalar approximation to the non-commutative operator framework of quantum information theory. We introduce rigorous quantum analogues of Sobolev and Hölder spaces defined through Fréchet differentiability in the Liouville representation and measured using the completely bounded (diamond) norm. Within this framework, we derive an explicit asymptotic expansion of the approximation error and identify the fundamental mechanisms governing convergence. The expansion separates integer-order differential contributions, fractional corrections associated with limited regularity, and intrinsically non-commutative effects arising from operator algebra structure. We also establish a sharp remainder estimate with explicit dependence on the regularity of the channel and the dimension of the underlying Hilbert space. Several applications demonstrate the scope of the theory. These include a quantum central limit theorem describing the fluctuation regime of quantum neural network operators, an optimal interpolation method based on operator geometric means, and a convergence acceleration procedure inspired by Richardson extrapolation. The results provide a rigorous mathematical foundation for the asymptotic analysis of quantum neural network models and establish a direct connection between classical approximation theory, operator algebras, and quantum information science, with implications for quantum algorithms and quantum machine learning. Comments: 37 pages Subjects: Quantum Physics (quant-ph); Mathematical Physics (math-ph) MSC classes: 41A60, 47L90, 81P45, 46L07 Cite as: arXiv:2603.18033 [quant-ph]   (or arXiv:2603.18033v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.18033 Focus to learn more Submission history From: Rômulo Damasclin Chaves Dos Santos [view email] [v1] Mon, 9 Mar 2026 18:12:57 UTC (34 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 Change to browse by: math math-ph math.MP References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Quantum
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    ◌ Quantum Computing
    Published
    Mar 20, 2026
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    Mar 20, 2026
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