# QUBO transformation using Eigenvalue Decomposition

arXiv: https://arxiv.org/abs/2106.10532 · HTML: https://mlsanity.com/paper/2106.10532/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions](https://mlsanity.com/paper/2107.11695/) (2021)

## Abstract (arXiv)

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.