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Derivation of QUBO Formulations for Sparse Estimation [PDF]
Journal of the Physical Society of Japan, 2020 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
Tomohiro Yokota +3 more
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QUBO Formulation for Sparse Sensor Placement for Classification
The demand for facial recognition technology has grown in various sectors over the past decade, but the need for efficient feature selection methods is crucial due to high-dimensional data complexity. This paper explores the potential of quantum computing for Sparse Sensor Placement optimisation (SSPO) in facial image classification.
Melanie R. van Dommelen +1 more
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QUBO formulations for training machine learning models [PDF]
Scientific Reports, 2021 AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models ...
Prasanna Date +2 more
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HUBO and QUBO models for prime factorization
Scientific Reports, 2023 AbstractThe security of the RSA cryptosystem is based on the difficulty of factoring a large number N into prime numbers $$p$$ p and $$q$$ q satisfying $$N=p\times q$$ N = p ×
Kyungtaek Jun, Hyunju Lee
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Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
Applied Sciences, 2022 In this work, we introduce a new workflow to solve portfolio optimization problems on annealing platforms. We combine a classical preprocessing step with a modified unconstrained binary optimization (QUBO) model and evaluate it using simulated annealing (
Jonas Lang +2 more
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Frontiers in Physics, 2021 Quantum annealing is a global optimization algorithm that uses the quantum tunneling effect to speed-up the search for an optimal solution. Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of ...
Sizhuo Yu, Sizhuo Yu, Tahar Nabil
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A QUBO formulation for the Tree Containment problem
Theoretical Computer Science, 2023 Phylogenetic (evolutionary) trees and networks are leaf-labeled graphs that are widely used to represent the evolutionary relationships between entities such as species, languages, cancer cells, and viruses. To reconstruct and analyze phylogenetic networks, the problem of deciding whether or not a given rooted phylogenetic network embeds a given rooted
Michael J. Dinneen +2 more
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Trip Planning Based on subQUBO Annealing
IEEE Access, 2023 The trip planning problem (TPP) can be formulated as a combinatorial optimization problem that searches for the best route to visit a series of landmarks and hotels.
Tatsuya Noguchi +3 more
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Automatic Conversion of MiniZinc Programs to QUBO
CoRR, 2023 Obtaining Quadratic Unconstrained Binary Optimisation models for various optimisation problems, in order to solve those on physical quantum computers (such as the the DWave annealers) is nowadays a lengthy and tedious process that requires one to remodel all problem variables as binary variables and squeeze the target function and the constraints into ...
Armin Wolf, Cristian Grozea
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QUBOs for Sorting Lists and Building Trees
CoRR, 2022 We show that the fundamental tasks of sorting lists and building search trees or heaps can be modeled as quadratic unconstrained binary optimization problems (QUBOs). The idea is to understand these tasks as permutation problems and to devise QUBOs whose solutions represent appropriate permutation matrices.
Christian Bauckhage +2 more
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