Gerhard Stenzel
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View article: Evolutionary-Based Circuit Optimization for Distributed Quantum Computing
Evolutionary-Based Circuit Optimization for Distributed Quantum Computing Open
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 Quantum Computing (DQC) paradigm. We evaluate our approach for a…
View article: Solving graph problems using permutation-invariant quantum machine learning
Solving graph problems using permutation-invariant quantum machine learning Open
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 quantum machine learning, the ansatz can be tuned to correspon…
View article: Quantum Circuit Construction and Optimization through Hybrid Evolutionary Algorithms
Quantum Circuit Construction and Optimization through Hybrid Evolutionary Algorithms Open
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 first approach, we combine the evolutionary algorithm with an o…
View article: Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis
Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis Open
Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains c…
View article: PIMAEX: Multi-Agent Exploration through Peer Incentivization
PIMAEX: Multi-Agent Exploration through Peer Incentivization Open
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 reinforcement learning. To address this issue, this work proposes a…
View article: PIMAEX: Multi-Agent Exploration Through Peer Incentivization
PIMAEX: Multi-Agent Exploration Through Peer Incentivization Open
While exploration in single-agent reinforcement learning has been studied extensively in recent years, consid-erably less work has focused on its counterpart in multi-agent reinforcement learning. To address this issue, this work proposes …
View article: Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting Open
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repe…
View article: QMamba: Quantum Selective State Space Models for Text Generation
QMamba: Quantum Selective State Space Models for Text Generation Open
This book contains the proceedings of the 17th International Conference on Agents and Artificial Intelligence. This year, ICAART is held in Porto, Portugal, on February 23-25, 2025. As usual it is sponsored by the Institute for Systems and…
View article: Optimizing Sensor Redundancy in Sequential Decision-Making Problems
Optimizing Sensor Redundancy in Sequential Decision-Making Problems Open
Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simulated environments), sensors are essential for measuring th…
View article: A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning Open
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 reduce the use of computational resources. However, in the …
View article: Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting Open
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repe…
View article: Quantum Denoising Diffusion Models
Quantum Denoising Diffusion Models Open
In recent years, machine learning models like DALL-E, Craiyon, and Stable Diffusion have gained significant attention for their ability to generate high-resolution images from concise descriptions. Concurrently, quantum computing is showin…
View article: Self-Replicating Prompts for Large Language Models: Towards Artificial Culture
Self-Replicating Prompts for Large Language Models: Towards Artificial Culture Open
We consider various setups where large language models (LLMs) communicate solely with themselves or other LLMs. In accordance with similar results known for program representations (like λ-expressions or automata), we observe a natural ten…
View article: Self-Adaptive Robustness of Applied Neural-Network-Soups
Self-Adaptive Robustness of Applied Neural-Network-Soups Open
We consider the dynamics of artificial chemistry systems consisting of small, interacting neural-network particles. Although recent explorations into properties of such systems have shown interesting phenomena, like self-replication tenden…