Any finite combinatorial problem with a known logical circuit can be encoded as a tensor network whose contraction defines an explicit, though generally inefficient, solution equation.
Tensor Network Based HOBO Solver
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abstract
In the field of quantum computing, combinatorial optimization problems are typically addressed using QUBO (Quadratic Unconstrained Binary Optimization) solvers. However, these solvers are often insufficient for tackling higher-order problems. In this paper, we introduce a novel and efficient solver designed specifically for HOBO (Higher-Order Binary Optimization) problem settings. Our approach leverages advanced techniques to effectively manage the complexity and computational demands associated with high-dimensional optimization tasks. The proposed solver is a promising tool with significant potential for future extensions in terms of formulation. This solver holds promising potential for a wide range of applications in quantum computing.
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Explicit Solution Equation for Every Combinatorial Problem via Tensor Networks: MeLoCoToN
Any finite combinatorial problem with a known logical circuit can be encoded as a tensor network whose contraction defines an explicit, though generally inefficient, solution equation.