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QUBO transformation using Eigenvalue Decomposition

2021-06-19 16:58:15
Amit Verma, Mark Lewis
arXiv_AI
     
Abstract

Quadratic Unconstrained Binary Optimization (QUBO) is a general-purpose modeling framework for combinatorial optimization problems and is a requirement for quantum annealers. This paper utilizes the eigenvalue decomposition of the underlying Q matrix to alter and improve the search process by extracting the information from dominant eigenvalues and eigenvectors to implicitly guide the search towards promising areas of the solution landscape. Computational results on benchmark datasets illustrate the efficacy of our routine demonstrating significant performance improvements on problems with dominant eigenvalues.

Abstract (translated)

URL

https://arxiv.org/abs/2106.10532

PDF

https://arxiv.org/pdf/2106.10532.pdf

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