1 citations · 1 across the 4 of their papers we have counts for
5 papers
Formulating Multistage Cutting Stock Problems as QUBO
Marcel Seelbach Benkner, Chiara Capecci, Sebastian Nagies +6
Cutting stock problems are of large relevance to a variety of industry branches. Here, we present a linear programming formulation for a restricted version of the multistage 2D cut…
Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms
Natacha Kuete Meli, Shuteng Wang, Marcel Seelbach Benkner +5
Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, optimisation theory, machine learning and quantum computing. It has high pot…
Practical Noise Mitigation for Quantum Annealing via Dynamical Decoupling: Toward Industry-Relevant Optimization using Trapped Ions
Sebastian Nagies, Chiara Capecci, Marcel Seelbach Benkner +6
Quantum annealing is a framework for solving combinatorial optimization problems. While it offers a promising path towards a practical application of quantum hardware, its performa…
Compensating connectivity restrictions in quantum annealers via splitting and linearization techniques
Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik +2
Current quantum annealing experiments often suffer from restrictions in connectivity in the sense that only certain qubits can be coupled to each other. The most common strategy to…
QuCOOP: A Versatile Framework for Solving Composite and Binary-Parametrised Problems on Quantum Annealers
Natacha Kuete Meli, Vladislav Golyanik, Marcel Seelbach Benkner +1
There is growing interest in solving computer vision problems such as mesh or point set alignment using Adiabatic Quantum Computing (AQC). Unfortunately, modern experimental AQC de…