Thomas Gabor
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View article: Circuit Partitioning for the Quantum Internet
Circuit Partitioning for the Quantum Internet Open
In a quantum internet, quantum processing units (QPUs) with varying architectures and capabilities may be connected through quantum communication channels, enabling new applications such as distributed quantum computing (DQC), a paradigm i…
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: Towards Scalable Lottery Ticket Networks using Genetic Algorithms
Towards Scalable Lottery Ticket Networks using Genetic Algorithms Open
Building modern deep learning systems that are not just effective but also efficient requires rethinking established paradigms for model training and neural architecture design. Instead of adapting highly overparameterized networks and sub…
View article: Time-Aware Qubit Assignment and Circuit Optimization for Distributed Quantum Computing
Time-Aware Qubit Assignment and Circuit Optimization for Distributed Quantum Computing Open
The emerging paradigm of distributed quantum computing promises a potential solution to scaling quantum computing to currently unfeasible dimensions. While this approach itself is still in its infancy, and many obstacles must still be over…
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: 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: Discriminative reward co-training
Discriminative reward co-training Open
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL), we introduce an imitation buffer to store beneficial trajectories …
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: Finding Strong Lottery Ticket Networks with Genetic Algorithms
Finding Strong Lottery Ticket Networks with Genetic Algorithms Open
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 weights - already performs as well for a given task as the tr…
View article: Solving Max-3SAT Using QUBO Approximation
Solving Max-3SAT Using QUBO Approximation Open
As contemporary quantum computers do not possess error correction, any calculation performed by these devices can be considered an involuntary approximation. To solve a problem on a quantum annealer, it has to be expressed as an instance o…
View article: Emergence in Multi-Agent Systems: A Safety Perspective
Emergence in Multi-Agent Systems: A Safety Perspective Open
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 to catastrophic system failures. To formally define these …
View article: Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning Open
Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain scenarios. However, practical benefits require further val…
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: Using an Evolutionary Algorithm to Create (MAX)-3SAT QUBOs
Using an Evolutionary Algorithm to Create (MAX)-3SAT QUBOs Open
A common way of solving satisfiability instances with quantum methods is to transform these instances into instances of QUBO, which in itself is a potentially difficult and expensive task. State-of-the-art transformations from MAX-3SAT to …
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: REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning Open
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of validating RL models based on their optimal behavior learne…
View article: Towards Federated Learning on the Quantum Internet
Towards Federated Learning on the Quantum Internet Open
While the majority of focus in quantum computing has so far been on monolithic quantum systems, quantum communication networks and the quantum internet in particular are increasingly receiving attention from researchers and industry alike.…
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…
View article: Challenges for Reinforcement Learning in Quantum Circuit Design
Challenges for Reinforcement Learning in Quantum Circuit Design Open
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises bo…
View article: Discriminative Reward Co-Training
Discriminative Reward Co-Training Open
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL), we introduce an imitation buffer to store beneficial trajectories …
View article: Applying an Evolutionary Algorithm to Minimize Teleportation Costs in Distributed Quantum Computing
Applying an Evolutionary Algorithm to Minimize Teleportation Costs in Distributed Quantum Computing Open
By connecting multiple quantum computers (QCs) through classical and quantum channels, a quantum communication network can be formed. This gives rise to new applications such as blind quantum computing, distributed quantum computing, and q…
View article: Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines Open
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the p…
View article: Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations
Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations Open
One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems…
View article: Pattern QUBOs: Algorithmic construction of 3SAT-to-QUBO transformations
Pattern QUBOs: Algorithmic construction of 3SAT-to-QUBO transformations Open
3SAT instances need to be transformed into instances of Quadratic Unconstrained Binary Optimization (QUBO) to be solved on a quantum annealer. Although it has been shown that the choice of the 3SAT-to-QUBO transformation can impact the sol…
View article: Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study Open
To solve 3SAT instances on quantum annealers they need to be transformed to an instance of Quadratic Unconstrained Binary Optimization (QUBO). When there are multiple transformations available, the question arises whether different transfo…