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Research PaperResearchia:202608.05096

CNOT-Distance is NP-complete under all-to-all connectivity

Antonio Acuaviva

Abstract

Given $A\in\operatorname{GL}(N,2)$ and an integer $K$, we ask whether $A$ can be implemented by at most $K$ CNOT gates on fixed labelled wires with all-to-all connectivity. We prove that this problem is NP-complete. From a finite simple graph $G=(V,E)$, we construct an upper-unitriangular matrix $A_G\in\operatorname{GL}(2|V|+|E|+1,2)$ satisfying $\ell_{\mathrm{CNOT}}(A_G)=2|V|+2|E|+Ο„(G)$, where $Ο„(G)$ is the minimum vertex-cover size. Each target matrix has $O(N)$ nonzero entries and row Hamming...

Submitted: August 5, 2026Subjects: Quantum Physics; Quantum Computing

Description / Details

Given A∈GL⁑(N,2)A\in\operatorname{GL}(N,2) and an integer KK, we ask whether AA can be implemented by at most KK CNOT gates on fixed labelled wires with all-to-all connectivity. We prove that this problem is NP-complete. From a finite simple graph G=(V,E)G=(V,E), we construct an upper-unitriangular matrix AG∈GL⁑(2∣V∣+∣E∣+1,2)A_G\in\operatorname{GL}(2|V|+|E|+1,2) satisfying β„“CNOT(AG)=2∣V∣+2∣E∣+Ο„(G)\ell_{\mathrm{CNOT}}(A_G)=2|V|+2|E|+Ο„(G), where Ο„(G)Ο„(G) is the minimum vertex-cover size. Each target matrix has O(N)O(N) nonzero entries and row Hamming weight at most four. The lower bound unfolds an arbitrary CNOT circuit into an XOR directed acyclic graph and applies projection--contraction operations, allowing cancellation and unrestricted reuse of intermediate parities. For this family, the optimum is unchanged by any finite number of clean or borrowed ancillary wires that must be restored. A polynomial-time decoder further yields NP-hardness of approximation within every fixed additive constant and, through an L-reduction from Minimum Vertex Cover on cubic graphs, APX-hardness of the associated CNOT-circuit optimisation problem.


Source: arXiv:2608.03825v1 - http://arxiv.org/abs/2608.03825v1 PDF: https://arxiv.org/pdf/2608.03825v1 Original Link: http://arxiv.org/abs/2608.03825v1

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Date:
Aug 5, 2026
Topic:
Quantum Computing
Area:
Quantum Physics
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