15 citations · 32 across the 11 of their papers we have counts for
6 papers · 1 filter
Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning
Kevin W. Aoun, Florian J. Kiwit, Carlos A. Riofrío +4
A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data int…
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…
Quantum Computing for Automotive Applications
Carlos A. Riofrío, Johannes Klepsch, Jernej Rudi Finžgar +7
Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engin…
Assessing the Requirements for Industry Relevant Quantum Computation
Anna M. Krol, Marvin Erdmann, Ewan Munro +2
In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem o…
Towards Application-Aware Quantum Circuit Compilation
Nils Quetschlich, Florian J. Kiwit, Maximilian A. Wolf +4
Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To t…
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…