6 citations · 12 across the 4 of their papers we have counts for
6 papers
Quantum-Enhanced Selection Operators for Evolutionary Algorithms
David Von Dollen, Sheir Yarkoni, Daniel Weimer +2
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be ob…
Multi-car paint shop optimization with quantum annealing
Sheir Yarkoni, Alex Alekseyenko, Michael Streif +3
We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the opti…
Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling
David Von Dollen, Florian Neukart, Daniel Weimer +1
Within machine learning model evaluation regimes, feature selection is a technique to reduce model complexity and improve model performance in regards to generalization, model fit,…
Quantum-assisted finite-element design optimization
Dyon van Vreumingen, Florian Neukart, David Von Dollen +4
Quantum annealing devices such as the ones produced by D-Wave systems are typically used for solving optimization and sampling tasks, and in both academia and industry the characte…
Quantum-enhanced reinforcement learning for finite-episode games with discrete state spaces
Florian Neukart, David Von Dollen, Christian Seidel +1
Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as…
Traffic flow optimization using a quantum annealer
Florian Neukart, Gabriele Compostella, Christian Seidel +3
Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such a…