arXiv subjects

Florian Kiwit

Publications and source records attributed to Florian Kiwit.

3 recordsLinked to original sources

arXiv2025

BenchQC -- Scalable and modular benchmarking of industrial quantum computing applications

We present BenchQC, a research project funded by the state of Bavaria, which promotes an application-centric perspective for benchmarking real-world quantum applications. Diverse use cases from industry consortium members are the starting point of a benchmarking workflow, that builds on the open-source platform QUARK, encompassing the full quantum software stack from the hardware provider interface to the application layer. By identifying and evaluating key metrics across the entire pipeline, we aim to uncover meaningful trends, provide systematic guidance on quantum utility, and distinguish promising research directions from less viable approaches. Ultimately, this initiative contributes to the broader effort of establishing reliable benchmarking standards that drive the transition from experimental demonstrations to practical quantum advantage.

quant-ph↗

arXiv2024

Quantum Computing for Automotive Applications

Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engineering, being particularly promising. This chapter investigates state-of-the-art quantum algorithms to enhance efficiency, accuracy, and scalability across the automotive value chain. We explore recent advances in quantum optimization, machine learning, and numerical and chemistry simulations, highlighting their potential and limitations. We identify and discuss key challenges in near-term and fault-tolerant algorithms and their practical use in industrial applications. While quantum algorithms show potential in many application domains, current noisy intermediate-scale quantum hardware limits scale and, thus, business benefits. In the long term, fault-tolerant systems promise theoretical speedups; however, they also require further progress in hardware and software (e.g., related to error correction and data loading). We expect that with this progress, significant practical benefits will emerge eventually.

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arXiv2023

Pooling techniques in hybrid quantum-classical convolutional neural networks

Quantum machine learning has received significant interest in recent years, with theoretical studies showing that quantum variants of classical machine learning algorithms can provide good generalization from small training data sizes. However, there are notably no strong theoretical insights about what makes a quantum circuit design better than another, and comparative studies between quantum equivalents have not been done for every type of classical layers or techniques crucial for classical machine learning. Particularly, the pooling layer within convolutional neural networks is a fundamental operation left to explore. Pooling mechanisms significantly improve the performance of classical machine learning algorithms by playing a key role in reducing input dimensionality and extracting clean features from the input data. In this work, an in-depth study of pooling techniques in hybrid quantum-classical convolutional neural networks (QCCNNs) for classifying 2D medical images is performed. The performance of four different quantum and hybrid pooling techniques is studied: mid-circuit measurements, ancilla qubits with controlled gates, modular quantum pooling blocks and qubit selection with classical postprocessing. We find similar or better performance in comparison to an equivalent classical model and QCCNN without pooling and conclude that it is promising to study architectural choices in QCCNNs in more depth for future applications.

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