We propose a quadratic unconstrained binary optimization (QUBO) formulation of the l1-norm, which enables us to perform sparse estimation of Ising-type annealing methods such as quantum annealing. The QUBO formulation is derived using the Legendre transformation and the Wolfe theorem, which have recently been employed to derive the QUBO formulations of ReLU-type functions. It is shown that a simple application of the derivation method to the l1-norm case results in a redundant variable. Finally a simplified QUBO formulation is obtained by removing the redundant variable.
Cite
@article{arxiv.2001.03715,
title = {Derivation of QUBO formulations for sparse estimation},
author = {Tomohiro Yokota and Makiko Konoshima and Hirotaka Tamura and Jun Ohkubo},
journal= {arXiv preprint arXiv:2001.03715},
year = {2020}
}