28 citations · 28 across the 6 of their papers we have counts for
6 papers
Quantum Computing -- Strategic Recommendations for the Industry
Marvin Erdmann, Lukas Karch, Abhishek Awasthi +9
This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in t…
Solving a real-world modular logistic scheduling problem with a quantum-classical metaheuristics
Florian Krellner, Abhishek Awasthi, Nico Kraus +3
This study evaluates the performance of a quantum-classical metaheuristic and a traditional classical mathematical programming solver, applied to two mathematical optimization mode…
Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
Daniëlle Schuman, Mark V. Seebode, Tobias Rohe +5
Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increa…
Generative-enhanced optimization for knapsack problems: an industry-relevant study
Yelyzaveta Vodovozova, Abhishek Awasthi, Caitlin Jones +4
Optimization is a crucial task in various industries such as logistics, aviation, manufacturing, chemical, pharmaceutical, and insurance, where finding the best solution to a probl…
Real World Application of Quantum-Classical Optimization for Production Scheduling
Abhishek Awasthi, Nico Kraus, Florian Krellner +1
This work is a benchmark study for quantum-classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on di…
A performance characterization of quantum generative models
Carlos A. Riofrío, Oliver Mitevski, Caitlin Jones +6
Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is…