Results 41 to 50 of about 2,283 (206)
Physical Review Research, 2019 We analyze the transformation of quadratic unconstrained binary optimization (QUBO) from its conventional Boolean presentation into an equivalent spin-glass problem with coupled ±1 spin variables exposed to a site-dependent external field.
Stefan Boettcher
doaj +1 more source
AQUA: An Agile Process to Develop Quantum Annealing Applications
IEEE AccessQuadratic unconstrained binary optimization (QUBO) is a field of operations research that is attracting growing interest due to the recent availability of quantum hardware targeted at solving QUBO problems.
Lodovica Marchesi +2 more
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Stochastic‐MTJ Sampler Arrays for In‐Array Monte–Carlo Estimation
Advanced Electronic Materials, EarlyView.A low‐energy‐barrier magnetic tunnel junction array is operated as a probability‐domain sampler: each cell's random switching, programmed through a shared digital‐to‐analog converter, makes the per‐column multiply–accumulate an unbiased Monte–Carlo expectation estimator that returns both a mean and a calibrated uncertainty.
Ran Zhang +6 more
wiley +1 more source
Optimum-Preserving QUBO Parameter Compression
, 2023 Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least because they can be approached using contemporary quantum annealing or classical hardware acceleration.
Gerlach, Thore +2 more
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AIChE Journal, EarlyView.ABSTRACT Conceptual process design combines discrete configuration choices with continuous operating decisions, often yielding difficult mixed‐integer nonlinear or simulation‐based optimization problems. This work presents an exploratory computational assessment of Ising‐based solvers, simulated annealing, quantum annealing, and entropy computing, as ...
Yirang Park, David E. Bernal Neira
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley +1 more source
Mean field approximation for solving QUBO problems
PLOS ONE, 2022 The Quadratic Unconstrained Binary Optimization (QUBO) problem is NP-hard. Some exact methods like the Branch-and-Bound algorithm are suitable for small problems. Some approximations like stochastic simulated annealing for discrete variables or mean-field annealing for continuous variables exist for larger ones, and quantum computers based on the ...
Máté Tibor Veszeli, Gábor Vattay
openaire +6 more sources
A Comprehensive Comparative Study of Active Learning Schemes for Nanophotonics Design
Advanced Intelligent Discovery, EarlyView.Active learning (AL) strategies are benchmarked for the binary design of planar multilayer nanophotonic structures. Factorization machines combined with quantum annealing (QA) become effective as dimensionality increases. Hybrid QA provides the strongest results for 100‐layer problems, highlighting the importance of optimization method selection in ...
Serang Jung +10 more
wiley +1 more source
A QUBO formulation for top-τ eigencentrality nodes
PLOS ONE, 2022 The efficient calculation of the centrality or “hierarchy” of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eigenvector centrality (aka eigencentrality) is quickly becoming a good metric for centrality due to both its simplicity and fidelity.
Prosper D. Akrobotu +3 more
openaire +4 more sources
Black Box Optimization Using QUBO and the Cross Entropy Method
, 2023 Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box ...
Nüßlein, Jonas +5 more
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