Topological Witnesses in Hyperdimensional Space: An Empirical Separation of P and NP via Persistent Homology on the Minimum Circuit Size Problem

Este art´ıculo presenta un marco metodol´ogico nove-doso que cruza el C´omputo Hiperdimensional (HDC) con la Topolog´ıa Algebraica para abordar el prob-lema P ̸= NP . Mediante un agente de validaci´on aut´onomo, proponemos el uso de HDC como M´etrica de Entrop´ıa. Mapeamos instancias del Minimum Circuit Size Problem (MCSP) a un espacio de alta dimensionalidad y utilizamos Homolog´ıa Persistente para identificar un Invariante Topol´ogico (Wn). Los resultados emp´ıricos computacionales para n ≤ 10 revelan una separaci´on estricta del 100%, evadiendo asint´oticamente las barreras cl´asicas de Relativiza-tion, Natural Proofs y Algebraization.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-16
DOI
https://doi.org/10.5281/zenodo.20709134
Primary Topic
Topological and Geometric Data Analysis
Type
preprint
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preprint

Topological Witnesses in Hyperdimensional Space: An Empirical Separation of P and NP via Persistent Homology on the Minimum Circuit Size Problem

Rodrigo Andres Espinosa M, OMNI Research
Zenodo (CERN European Organization for Nuclear Research)
Topological and Geometric Data Analysis
preprint

Topological Witnesses in Hyperdimensional Space: An Empirical Separation of P and NP via Persistent Homology on the Minimum Circuit Size Problem

Rodrigo Andres Espinosa M, OMNI Research
preprint en

Abstract

Este art´ıculo presenta un marco metodol´ogico nove-doso que cruza el C´omputo Hiperdimensional (HDC) con la Topolog´ıa Algebraica para abordar el prob-lema P ̸= NP . Mediante un agente de validaci´on aut´onomo, proponemos el uso de HDC como M´etrica de Entrop´ıa. Mapeamos instancias del Minimum Circuit Size Problem (MCSP) a un espacio de alta dimensionalidad y utilizamos Homolog´ıa Persistente para identificar un Invariante Topol´ogico (Wn). Los resultados emp´ıricos computacionales para n ≤ 10 revelan una separaci´on estricta del 100%, evadiendo asint´oticamente las barreras cl´asicas de Relativiza-tion, Natural Proofs y Algebraization.

Zenodo (CERN European Organization for Nuclear Research)
Topological and Geometric Data Analysis
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