Quantum approximate multi-objective optimization. [PDF]
Nat Comput SciKotil A +6 more
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Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges. [PDF]
ACS Energy LettKim S +5 more
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Digital Annealer for quadratic unconstrained binary optimization: A comparative performance analysis
Applied Soft Computing Journal, 2022 Digital Annealer (DA) is a computer architecture designed for tackling combinatorial optimization problems formulated as quadratic unconstrained binary optimization (QUBO) models. In this paper, we present the results of an extensive computational study to evaluate the performance of DA in a systematic way in comparison to multiple state-of-the-art ...
Oylum Şeker, Merve Bodur
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Quadratic unconstrained binary optimization problem preprocessing: Theory and empirical analysis [PDF]
Networks, 2017 The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a unifying model for representing a wide range of combinatorial optimization problems, and for linking a variety of disciplines that face these problems. A new class of quantum annealing computer that maps QUBO onto a physical qubit network structure with specific size and edge ...
Mark Lewis, Fred Glover
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Extremal Optimization for Quadratic Unconstrained Binary Problems
Physics Procedia, 2015 AbstractWe present an implementation of τ-EO for quadratic unconstrained binary optimization (QUBO) problems. To this end, we transform modify QUBO from its conventional Boolean presentation into a spin glass with a random external field on each site.
Stefan Boettcher
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High‐throughput FPGA implementation for quadratic unconstrained binary optimization
Concurrency and Computation: Practice and Experience, 2021AbstractQuadratic unconstrained binary optimization (QUBO) is a combinatorial optimization problem. Since various NP‐hard problems such as the traveling salesman problem can be formulated as a QUBO instance, QUBO is used with a wide range of applications.
Hiroshi Kagawa +8 more
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Probabilistic reasoning as quadratic unconstrained binary optimization
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022Probabilistic reasoning is an important tool for using uncertainty in AI, especially for automated reasoning. Partial probability assessments are a way of expressing partial probabilistic knowledge on a set of events. These assessments contain only the information about "interesting"events (hence it can be easily assessed by an expert).
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Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO)
Journal of Heuristics, 2007We present a family of local-search-based heuristics for Quadratic Unconstrained Binary Optimization (QUBO), all of which start with a (possibly fractional) initial point, sequentially improving its quality by rounding or switching the value of one variable, until arriving to a local optimum. The effects of various parameters on the efficiency of these
Endre Boros, Peter L Hammer
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A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization
IEEE Transactions on Emerging Topics in Computational IntelligenceHongzong Li, Jun Wang
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Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system
2017 International Joint Conference on Neural Networks (IJCNN), 2017The problems of Artificial intelligence (AI) naturally maps to NP-hard optimization problems. This trend has significance to achieve human-level computation capability from machines. This computational ability can be achieved by developing evolutionary algorithms or mapping those evolutionary algorithms onto new generation computing systems: Quantum or
Md. Zahangir Alom +4 more
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