Quick Navigation
Topics
Quantum Networks
Quantum Machine Learning
Quantum Foundations
Quantum Kolmogorov--Arnold representation theorem for continuous unitary-valued maps
arXiv
Authors: Sviatoslav V. Dzhenzher
Year
2026
Paper ID
72473
Status
Preprint
Abstract Read
~2 min
Abstract Words
173
Citations
N/A
Abstract
The classical Kolmogorov--Arnold representation theorem states that any continuous multivariate function can be exactly decomposed into a finite composition of univariate continuous functions and addition operations. This foundational result has recently inspired the development of Kolmogorov--Arnold Networks (KANs) in classical machine learning, as well as their extensions into the quantum domain (QKANs). In this paper, we establish two quantum analogues of the Kolmogorov--Arnold representation theorem for continuous unitary-valued maps of several variables within an open 1-neighbourhood of the identity matrix O1\(mathbf{I} \subset \mathcal{U}(n)\). First, we prove a representation theorem that yields an exact additive decomposition inside the matrix exponent of anti-Hermitian-valued maps. Second, due to the non-commutative nature of quantum operators, we derive a factorised version expressing the target unitary map as a finite sequential product of univariate matrix exponentials. Finally, we provide a concrete topological counterexample based on the lifting property of mathcal{SU}(2) to demonstrate that these local representation theorems cannot be globally extended to the entire unitary group mathcal{U}(n) without encountering fundamental structural obstructions.
Why This Paper Matters
- This paper contributes to the Quantum Networks research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- The classical Kolmogorov--Arnold representation theorem states that any continuous multivariate function can be exactly decomposed into a finite composition of univariate...
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.