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20192025
most citedDIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training

1 citations · 1 across the 15 of their papers we have counts for

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quant-ph2025

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…

quant-ph2025

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…

quant-ph2024

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…

quant-ph2024

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…

quant-ph2024

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…

quant-ph2024

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…