1 citations · 1 across the 2 of their papers we have counts for
7 papers
Grokking and epoch-wise double descent in quantum neural networks
Daniel PranjiÄ, 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…
Symmetry-based quantum algorithms for open-shop scheduling with hard constraints
Lennart Binkowski, Gereon KoÃmann, 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…
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…
Training robust and generalizable quantum models
Julian Berberich, Daniel Fink, Daniel PranjiÄ +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…
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…