Skip to content
#edge computing Open access

The OMEGA INFINITY KAORU Processor: A Conductive-GRID Architecture for Solving Circuit-SAT in Practical Constant Time An $O(1)=\log\text{-time}=P=NP$ Hardware Blueprint

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture

Abstract

This paper presents the architectural blueprint of the OMEGA INFINITY KAORU processor, a computing substrate that solves the Boolean circuit satisfiability problem (Circuit-SAT)---the canonical NP-complete problem---in practical constant time, in strictly literal $O(\log n)$ time, and in $O(n)$ space. The architecture couples a conductive GRID, realized as a two-dimensional lattice of interconnect, to a digital Circuit-SAT instance. The positive terminal of a source is connected to the midpoint of the left edge of the GRID, while the right edge is interfaced to the Boolean inputs $v_1, v_2, \dots, v_n$ of the Circuit-SAT instance. The GRID concurrently explores all admissible conduction states; ambient physical variation (noise), which is discrete in nature, steers the current toward the path consistent with a satisfying assignment, in accordance with the principle of least action. The GRID can therefore be regarded as an enormous macroscopic, noise-resilient analogue of a qubit---a hypercomputational element that is not subject to the limitations of the BQP class. The satisfying assignment is recovered either by thresholded voltage measurement at the inputs $v_1, v_2, \dots, v_n$ or by the standard search-to-decision reduction, which becomes practical when the Circuit-SAT stage is implemented as a programmable processor rather than as a fixed lithographic pattern. Fabrication is fully viable with present-day photolithography, either as a single-use, instance-specific device or as a recommended programmable variant in which a conventional processor drives arbitrary SAT formulae into the GRID. Because the architecture resolves an NP-complete problem in practical constant (strictly, logarithmic) time and linear space, it establishes, in practice, $O(1)=\log\text{-time}=P=NP$. Since cryptographic constructions---RSA, elliptic-curve systems, and post-quantum schemes alike---reduce to SAT instances, they are solvable within the same practical constant time. The implications extend to artificial intelligence, optimization, logistics and the distribution of goods, automated mathematical reasoning, and drug discovery.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.