13 citations · 30 across the 10 of their papers we have counts for
11 papers
Towards Scalable Lottery Ticket Networks using Genetic Algorithms
Julian Schönberger, Maximilian Zorn, Jonas Nüßlein +2
Building modern deep learning systems that are not just effective but also efficient requires rethinking established paradigms for model training and neural architecture design. In…
Grid Cost Allocation in Peer-to-Peer Electricity Markets: Benchmarking Classical and Quantum Optimization Approaches
David Bucher, Daniel Porawski, Benedikt Wimmer +4
This paper presents a novel optimization approach for allocating grid operation costs in Peer-to-Peer (P2P) electricity markets using Quantum Computing (QC). We develop a Quadratic…
Swarm Behavior Cloning
Jonas Nüßlein, Maximilian Zorn, Philipp Altmann +1
In sequential decision-making environments, the primary approaches for training agents are Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on model…
Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection
David Bucher, Daniel Porawski, Benedikt Wimmer +5
Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identi…
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
Gerhard Stenzel, Sebastian Zielinski, Michael Kölle +3
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit executio…
Sampling Problems on a Quantum Computer
Maximilian Balthasar Mansky, Jonas Nüßlein, David Bucher +3
Due to the advances in the manufacturing of quantum hardware in the recent years, significant research efforts have been directed towards employing quantum methods to solving probl…
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Jonas Stein, Michael Poppel, Philip Adamczyk +9
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning fr…
Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
Jonas Stein, Daniëlle Schuman, Magdalena Benkard +7
Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. With rapidly growing amounts of data and the omnipresenc…
Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
Jonas Stein, Farbod Chamanian, Maximilian Zorn +4
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimi…
Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming
Friedrich Wagner, Jonas Nüßlein, Frauke Liers
To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one h…
Case-Based Inverse Reinforcement Learning Using Temporal Coherence
Jonas Nüßlein, Steffen Illium, Robert Müller +2
Providing expert trajectories in the context of Imitation Learning is often expensive and time-consuming. The goal must therefore be to create algorithms which require as little ex…