52 citations · 96 across the 6 of their papers we have counts for
5 papers · 1 filter
Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK
Florian J. Kiwit, Maximilian A. Wolf, Marwa Marso +4
Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to…
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Florian J. Kiwit, Marwa Marso, Philipp Ross +3
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training t…
Quantum Computing Techniques for Multi-Knapsack Problems
Abhishek Awasthi, Francesco Bär, Joseph Doetsch +16
Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum…
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms
Martin J. A. Schuetz, J. Kyle Brubaker, Henry Montagu +6
We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble…
QUARK: A Framework for Quantum Computing Application Benchmarking
Jernej Rudi Finžgar, Philipp Ross, Leonhard Hölscher +2
Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances…