191 citations · 551 across the 134 of their papers we have counts for
25 papers · 1 filter
Topology-Guided Quantum GANs for Constrained Graph Generation
Tobias Rohe, Markus Baumann, Michael Poppel +3
Quantum computing (QC) promises theoretical advantages, benefiting computational problems that would not be efficiently classically simulatable. However, much of this theoretical s…
Circuit Partitioning for the Quantum Internet
Leo Sünkel, Thomas Gabor, Claudia Linnhoff-Popien
In a quantum internet, quantum processing units (QPUs) with varying architectures and capabilities may be connected through quantum communication channels, enabling new application…
Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing
Leo Sünkel, Jonas Stein, Jonas Nüßlein +2
Scaling quantum computers, i.e., quantum processing units (QPUs) to enable the execution of large quantum circuits is a major challenge, especially for applications that should pro…
Quantum Architecture Search for Solving Quantum Machine Learning Tasks
Michael Kölle, Simon Salfer, Tobias Rohe +2
Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains…
Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
Michael Kölle, Leonhard Klingert, Julian Schönberger +3
Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quan…
Evolutionary-Based Circuit Optimization for Distributed Quantum Computing
Leo Sünkel, Jonas Stein, Gerhard Stenzel +3
In this work, we evaluate an evolutionary algorithm (EA) to optimize a given circuit in such a way that it reduces the required communication when executed in the Distributed Quant…
Quantum Computer Benchmarking: An Explorative Systematic Literature Review
Tobias Rohe, Federico Harjes Ruiloba, Sabrina Egger +3
As quantum computing (QC) continues to evolve in hardware and software, measuring progress in this complex and diverse field remains a challenge. To track progress, uncover bottlen…
Mitigating Exponential Mixed Frequency Growth through Frequency Selection
Michael Poppel, David Bucher, Maximilian Zorn +4
Angle encoding has emerged as a popular feature map for embedding classical data into quantum models, naturally generating truncated Fourier series with universal function approxim…
Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification
Daniëlle Schuman, Mark V. Seebode, Tobias Rohe +5
Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increa…
Efficient QAOA Architecture for Solving Multi-Constrained Optimization Problems
David Bucher, Daniel Porawski, Maximilian Janetschek +4
This paper proposes a novel combination of constraint encoding methods for the Quantum Approximate Optimization Ansatz (QAOA). Real-world optimization problems typically consist of…
Time-Aware Qubit Assignment and Circuit Optimization for Distributed Quantum Computing
Leo Sünkel, Jonas Stein, Maximilian Zorn +2
The emerging paradigm of distributed quantum computing promises a potential solution to scaling quantum computing to currently unfeasible dimensions. While this approach itself is…
Clique detection using symmetry-restricted quantum circuits
Maximilian Balthasar Mansky, Tobias Rohe, Dmytro Bondarenko +2
We show the application of permutation-invariant quantum circuits to the clique problem. The experiment asks to label a clique through identification of the nodes in a larger subgr…
Analysis of quantum neural network performance via edge cases
Maximilian Balthasar Mansky, Tobias Rohe, Linus Menzel +2
We evaluate the particular performance of different quantum machine learning networks on a graph classification task. Quantum circuits with varying internal symmetry that completel…
Solving graph problems using permutation-invariant quantum machine learning
Maximilian Balthasar Mansky, Tobias Rohe, Gerhard Stenzel +7
Many computational problems are unchanged under some symmetry operation. In classical machine learning, this can be reflected with the layer structure of the neural network. In qua…
Quantum Circuit Construction and Optimization through Hybrid Evolutionary Algorithms
Leo Sünkel, Philipp Altmann, Michael Kölle +3
We apply a hybrid evolutionary algorithm to minimize the depth of circuits in quantum computing. More specifically, we evaluate two different variants of the algorithm. In the firs…
Surrogate Fitness Metrics for Interpretable Reinforcement Learning
Philipp Altmann, Céline Davignon, Maximilian Zorn +3
We employ an evolutionary optimization framework that perturbs initial states to generate informative and diverse policy demonstrations. A joint surrogate fitness function guides t…
IF-QAOA: A Penalty-Free Approach to Accelerating Constrained Quantum Optimization
David Bucher, Jonas Stein, Sebastian Feld +1
Traditional methods for handling (inequality) constraints in the Quantum Approximate Optimization Ansatz (QAOA) typically rely on penalty terms and slack variables, which increase…
Quality Diversity for Variational Quantum Circuit Optimization
Maximilian Zorn, Jonas Stein, Maximilian Balthasar Mansky +3
Optimizing the architecture of variational quantum circuits (VQCs) is crucial for advancing quantum computing (QC) towards practical applications. Current methods range from static…
Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
Michael Kölle, Alexander Feist, Jonas Stein +2
In recent years, neural networks (NNs) have driven significant advances in machine learning. However, as tasks grow more complex, NNs often require large numbers of trainable param…
Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis
Michael Kölle, Tom Bintener, Maximilian Zorn +4
Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potenti…
Accelerated VQE: Parameter Recycling for Similar Recurring Problem Instances
Tobias Rohe, Maximilian Balthasar Mansky, Michael Kölle +3
Training the Variational Quantum Eigensolver (VQE) is a task that requires substantial compute. We propose the use of concepts from transfer learning to considerably reduce the tra…
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…
PIMAEX: Multi-Agent Exploration through Peer Incentivization
Michael Kölle, Johannes Tochtermann, Julian Schönberger +3
While exploration in single-agent reinforcement learning has been studied extensively in recent years, considerably less work has focused on its counterpart in multi-agent reinforc…
Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs
Tobias Rohe, Florian Burger, Michael Kölle +3
The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent…