8 citations · 23 across the 13 of their papers we have counts for
13 papers
QAOA-Predictor: Forecasting Success Probabilities and Minimal Depths for Efficient Fixed-Parameter Optimization
Rodrigo Coelho, Georg Kruse, Jeanette Miriam Lorenz
Quantum Computing promises to solve complex combinatorial optimization problems more efficiently than classical methods, with the Quantum Approximate Optimization Algorithm (QAOA)…
Scalability Challenges in Variational Quantum Optimization under Stochastic Noise
Adelina Bärligea, Benedikt Poggel, Jeanette Miriam Lorenz
With rapid advances in quantum hardware, a central question is whether quantum devices with or without full error correction can outperform classical computers on practically relev…
AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review
Yuki Hagiwara, Octavia-Andreea Ciora, Maureen Monnet +2
The diagnosis of glaucoma plays a critical role in the management and treatment of this vision-threatening disease. Glaucoma is a group of eye diseases that cause blindness by dama…
Creating Automated Quantum-Assisted Solutions for Optimization Problems
Benedikt Poggel, Xiomara Runge, Adelina Bärligea +1
When trying to use quantum-enhanced methods for optimization problems, the sheer number of options inhibits its adoption by industrial end users. Expert knowledge is required for t…
Hybrid Meta-Solving for Practical Quantum Computing
Domenik Eichhorn, Maximilian Schweikart, Nick Poser +3
The advent of quantum algorithms has initiated a discourse on the potential for quantum speedups for optimization problems. However, several factors still hinder a practical realiz…
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization
Georg Kruse, Rodrigo Coehlo, Andreas Rosskopf +2
Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variatio…
Understanding the effects of data encoding on quantum-classical convolutional neural networks
Maureen Monnet, Nermine Chaabani, Theodora-Augustina Dragan +2
Quantum machine learning was recently applied to various applications and leads to results that are comparable or, in certain instances, superior to classical methods, in particula…
Quantum Patch-Based Autoencoder for Anomaly Segmentation
Maria Francisca Madeira, Alessandro Poggiali, Jeanette Miriam Lorenz
Quantum Machine Learning investigates the possibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to i…
Quantum Multi-Agent Reinforcement Learning for Aerial Ad-hoc Networks
Theodora-Augustina Drăgan, Akshat Tandon, Carsten Strobel +2
Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing qu…
Solving Combinatorial Optimization Problems with a Block Encoding Quantum Optimizer
Adelina Bärligea, Benedikt Poggel, Jeanette Miriam Lorenz
In the pursuit of achieving near-term quantum advantage for combinatorial optimization problems, the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum E…
Efficient Encodings of the Travelling Salesperson Problem for Variational Quantum Algorithms
Manuel Schnaus, Lilly Palackal, Benedikt Poggel +4
Routing problems are a common optimization problem in industrial applications, which occur on a large scale in supply chain planning. Due to classical limitations for solving NP-ha…
Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments
Georg Kruse, Theodora-Augustina Dragan, Robert Wille +1
Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcement learning (RL) that uses quantum submodules in the architecture of the algorithm. One branch of QRL focuses…
Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology
Jose Este Jaloveckas, Minh Tham Pham Nguyen, Lilly Palackal +2
The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learn…