10 citations · 54 across the 39 of their papers we have counts for
41 papers
Same Spectrum, Harder First-Order Training: Phase-Locking in Single- versus Multi-Qubit Re-Uploading Circuits
Michael Poppel, David Bucher, Maximilian Zorn +5
Angle-encoded variational quantum circuits implement truncated Fourier series. For one-dimensional inputs, a single-qubit re-uploading (serial) circuit represents every function th…
Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ansätze for Combinatorial Optimization
Tobias Rohe, Markus Baumann, Federico Harjes Ruiloba +3
Variational quantum algorithms (VQAs) for combinatorial optimization routinely employ entangling gates as a default design choice, yet the role of entanglement, in its amount and s…
Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
Michael Poppel, David Bucher, Maximilian Zorn +5
Variational quantum circuits with angle encoding implement truncated Fourier series, and architectures arranging qubits with encoding layers each -- sharing encoding budget…
Quantum Generator Kernels
Philipp Altmann, Maximilian Mansky, Maximilian Zorn +2
Quantum kernel methods offer significant theoretical benefits by rendering classically inseparable features separable in quantum space. Yet, the practical application of Quantum Ma…
Dynamic Incentivized Cooperation under Changing Rewards
Philipp Altmann, Thomy Phan, Maximilian Zorn +2
Peer incentivization (PI) is a popular multi-agent reinforcement learning approach where all agents can reward or penalize each other to achieve cooperation in social dilemmas. Des…
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…
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…
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 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…
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…
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…
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…
Finding Strong Lottery Ticket Networks with Genetic Algorithms
Philipp Altmann, Julian Schönberger, Maximilian Zorn +1
According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random we…
Sequential Hamiltonian Assembly: Enhancing the training of combinatorial optimization problems on quantum computers
Navid Roshani, Jonas Stein, Maximilian Zorn +3
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs). Much like in deep learning, vanishing gradients pose significan…
Emergence in Multi-Agent Systems: A Safety Perspective
Philipp Altmann, Julian Schönberger, Steffen Illium +5
Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior…
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
Michael Kölle, Daniel Seidl, Maximilian Zorn +3
Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain…
Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization
Michael Kölle, Karola Schneider, Sabrina Egger +5
In recent years, Multi-Agent Reinforcement Learning (MARL) has found application in numerous areas of science and industry, such as autonomous driving, telecommunications, and glob…
A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
Michael Kölle, Timo Witter, Tobias Rohe +3
Quantum Computing aims to streamline machine learning, making it more effective with fewer trainable parameters. This reduction of parameters can speed up the learning process and…
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…
MEDIATE: Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange
Philipp Altmann, Katharina Winter, Michael Kölle +3
Recent advances in multi-agent systems (MAS) have shown that incorporating peer incentivization (PI) mechanisms vastly improves cooperation. Especially in social dilemmas, communic…
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
Philipp Altmann, Céline Davignon, Maximilian Zorn +3
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of…
A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
Michael Kölle, Tom Schubert, Philipp Altmann +3
With recent advancements in quantum computing technology, optimizing quantum circuits and ensuring reliable quantum state preparation have become increasingly vital. Traditional me…