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Explicit Solution Equation for Every Combinatorial Problem via Tensor Networks: MeLoCoToN

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arxiv 2502.05981 v1 pith:5YHBIX3Q submitted 2025-02-09 cs.ET quant-ph

classification cs.ETquant-ph
keywords combinatorialproblemequationequationseverytensortimeallow
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In this paper we show that every combinatorial problem has an exact explicit equation that returns its solution. We present a method to obtain an equation that solves exactly any combinatorial problem, both inversion, constraint satisfaction and optimization, by obtaining its equivalent tensor network. This formulation only requires a basic knowledge of classical logical operators, at a first year level of any computer science degree. These equations are not necessarily computable in a reasonable time, nor do they allow to surpass the state of the art in computational complexity, but they allow to have a new perspective for the mathematical analysis of these problems. These equations computation can be approximated by different methods such as Matrix Product State compression. We also present the equations for numerous combinatorial problems. This work proves that, if there is a physical system capable of contracting in polynomial time the tensor networks presented, every NP-Hard problem can be solved in polynomial time.

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  1. Variational matrix product states for combinatorial optimization

    quant-ph 2025-12 conditional novelty 6.0 of 10

    Quantum-inspired product/matrix-product-state annealing embedded in iterated local search reports better MaxCut approximations than the ILS, LQA, GCS, and QAOA baselines tested, on graphs up to 50,000 vertices.

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