9 citations · 13 across the 4 of their papers we have counts for
4 papers
Typical Machine Learning Datasets as Low-Depth Quantum Circuits
Florian J. Kiwit, Bernhard Jobst, Andre Luckow +2
Quantum machine learning (QML) is an emerging field that investigates the capabilities of quantum computers for learning tasks. While QML models can theoretically offer advantages…
Path Matters: Industrial Data Meet Quantum Optimization
Lukas Schmidbauer, Carlos A. Riofrío, Florian Heinrich +4
Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware. Even for a fixed problem, the number of possible tra…
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…
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…