Reduction of interaction order in hard combinatorial optimization via conditionally independent degrees of freedom
Alexandru Ciobanu, David Dahmen, John Paul Strachan, Moritz Helias

TL;DR
This paper introduces a physics-inspired method using the renormalization group to reduce the interaction order in complex combinatorial optimization problems, enabling more efficient ground state computations.
Contribution
It develops a novel approach to transform third-order interactions into pairwise interactions while preserving the free energy, facilitating scalable optimization.
Findings
Effective pairwise interaction model preserves original energy spectrum.
Method enables efficient ground state reconstruction for disordered systems.
Approach offers new pathways for physical and technological optimization implementations.
Abstract
Combinatorial optimization problems have a broad range of applications and map to physical systems with complex dynamics. Among them, the 3-SAT problem is prominent due to its NP-complete nature. In physics terms, its solution corresponds to finding the ground state of a disordered Ising spin Hamiltonian with third-order, or tensor, interactions. The large growth of the number of third-order interactions with number of variables poses technical difficulties for the physical implementation of minimizers. Therefore, researchers have proposed quadratization techniques which reduce the order of the system, however, at the cost of including additional degrees of freedom. Their inclusion induces a drastic slow down in the minimization, which makes such procedures technically infeasible for large problems. In this work, we take a physics approach by employing the renormalization group to…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum many-body systems · Markov Chains and Monte Carlo Methods
