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QUBO formulation of l1-norm for Ising-type computers

Tomohiro Yokota, Makiko Konoshima, Hirotaka Takamura +1 more

IEICE Technical Report; IEICE Tech. Rep.Feb 26, 20200 citations

Learning QUBO Formulations from Data

Jonas Nüßlein, Sebastian Zielinski, Claudia Linnhoff–Popien

Communications in computer and information scienceJan 1, 20250 citations

Applications and Computational Advances for Solving the QUBO Model

Fred Glover, Gary A. Kochenberger, Yu Du

Springer eBooksJan 1, 202213 citations

Quadratic unconstrained binary optimization formulation for rectified-linear-unit-type functions

Go Sato, Makiko Konoshima, Takuya Ohwa +2 more

We propose a quadratic unconstrained binary optimization (QUBO) formulation of rectified-linear-unit (ReLU) type functions. Different from the q-loss function proposed by Denchev et al. [in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, edited by J. Langford and J. Pineau (Omnipress, Madison, USA, 2012)], a simple discussion based on the Legendre duality is not sufficient to obtain the QUBO formulation of ReLU-type functions. In addition to the Legendre duality, we employ the Wolfe duality, and the QUBO formulation of ReLU type is derived. The QUBO formulation is available in Ising-type annealing methods, including quantum annealing machines.We propose a quadratic unconstrained binary optimization (QUBO) formulation of rectified-linear-unit (ReLU) type functions. Different from the q-loss function proposed by Denchev et al. [in Proceedings of the 29th International Conference on Machine Learning, Edinburgh, edited by J. Langford and J. Pineau (Omnipress, Madison, USA, 2012)], a simple discussion based on the Legendre duality is not sufficient to obtain the QUBO formulation of ReLU-type functions. In addition to the Legendre duality, we employ the Wolfe duality, and the QUBO formulation of ReLU type is derived. The QUBO formulation is available in Ising-type annealing methods, including quantum annealing machines.

arXiv:1811.03829Physical review. EApr 4, 20197 citations

Derivation of QUBO Formulations for Sparse Estimation

Tomohiro Yokota, Makiko Konoshima, Hirotaka Tamura +1 more

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.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.

arXiv:2001.03715Journal of the Physical Society of JapanFeb 27, 20202 citations

QUBO formulation for the contact map overlap problem

Nicolas M. de Oliveira, Ricardo Martins de Abreu Silva, Wilson Rosa de Oliveira

Representing a given problem as a QUBO problem implies the possibility of running it in a quantum computational environment (generic or specific). The well-known problem of looking for similar functions in biological structures, especially of proteins, is of great interest in the field of Bioinformatics. We give a QUBO formulation for CMO protein problem. Experimental results validate this approach as an alternative to classical methods via combinatorial optimization. For the accomplishment of such experiments, we use the qbsolv tool.Representing a given problem as a QUBO problem implies the possibility of running it in a quantum computational environment (generic or specific). The well-known problem of looking for similar functions in biological structures, especially of proteins, is of great interest in the field of Bioinformatics. We give a QUBO formulation for CMO protein problem. Experimental results validate this approach as an alternative to classical methods via combinatorial optimization. For the accomplishment of such experiments, we use the qbsolv tool.

International Journal of Quantum InformationNov 22, 20187 citations

Numerical experiments of QUBO formulation for ReLU-type functions

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Numerical experiments of QUBO formulation for ReLU-type functions

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