19 citations · 26 across the 9 of their papers we have counts for
9 papers
Grokking and epoch-wise double descent in quantum neural networks
Daniel Pranjić, Marco Roth, Christian Tutschku
Grokking, the delayed transition from memorization to generalization, is a fundamental phenomenon in gradient-based learning, yet its dynamics within variational quantum machine le…
Experimental demonstration that qubits can be cloned at will, if encrypted with a single-use decryption key
Koji Yamaguchi, Leon Rullkötter, Ibrahim Shehzad +3
The no-cloning theorem forbids the creation of identical copies of qubits, thereby imposing strong limitations on quantum technologies. A recently-proposed protocol, encrypted clon…
Resource-efficient Variational Compilation of Block-Encodings
Leon Rullkötter, Sebastian Weber, Vamshi Mohan Katukuri +2
Block-encoding operators are one of the essential components in quantum algorithms based on Quantum Signal Processing. Their gate complexity largely determines the overall gate com…
Quantum Annealing based Feature Selection in Machine Learning
Daniel Pranjic, Bharadwaj Chowdary Mummaneni, Christian Tutschku
Feature selection is crucial for enhancing the accuracy and efficiency of machine learning (ML) models. This work investigates the utility of quantum annealing for the feature sele…
Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors
Daniel Pranjić, Florian Knäble, Philipp Kunst +6
Whether in fundamental physics, cybersecurity or finance, the detection of anomalies with machine learning techniques is a highly relevant and active field of research, as it poten…
Training robust and generalizable quantum models
Julian Berberich, Daniel Fink, Daniel Pranjić +2
Adversarial robustness and generalization are both crucial properties of reliable machine learning models. In this paper, we study these properties in the context of quantum machin…
Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
Dennis Klau, Marc Zöller, Christian Tutschku
This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into…
Benchmarking Automated Machine Learning Methods for Price Forecasting Applications
Horst Stühler, Marc-André Zöller, Dennis Klau +2
Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting proc…
Symmetry-based quantum algorithms for open-shop scheduling with hard constraints
Lennart Binkowski, Gereon Koßmann, Christian Tutschku +1
Encoding hard-constrained optimization problems into a variational quantum algorithm often turns out to be a challenging task. In this work, we provide a solution for the class of…