Asymptotic Expansions for Neural Network Approximations of Quantum Channels

TL;DR

This paper develops a comprehensive asymptotic theory for quantum neural network operators approximating quantum channels, extending classical approximation results to the quantum operator setting with applications in quantum information and machine learning.

Contribution

It introduces the Quantum Voronovskaya--Damasclin theorem, extending classical asymptotic approximation results to quantum channels within a non-commutative operator framework.

Findings

Derived explicit asymptotic expansion of approximation error.

Established a quantum central limit theorem for neural network operators.

Proposed an optimal interpolation method and convergence acceleration technique.

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 Fr\'echet 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.