| A survey on quantum reinforcement learning N Meyer, C Ufrecht, M Periyasamy, DD Scherer, A Plinge, C Mutschler arXiv preprint arXiv:2211.03464, 2022 | 26 | 2022 |
| Quantum policy gradient algorithm with optimized action decoding N Meyer, D Scherer, A Plinge, C Mutschler, M Hartmann International Conference on Machine Learning, 24592-24613, 2023 | 12 | 2023 |
| Incremental data-uploading for full-quantum classification M Periyasamy, N Meyer, C Ufrecht, DD Scherer, A Plinge, C Mutschler 2022 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2022 | 12 | 2022 |
| An empirical comparison of optimizers for quantum machine learning with spsa-based gradients M Wiedmann, M Hölle, M Periyasamy, N Meyer, C Ufrecht, DD Scherer, ... 2023 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2023 | 9 | 2023 |
| Quantum natural policy gradients: Towards sample-efficient reinforcement learning N Meyer, DD Scherer, A Plinge, C Mutschler, MJ Hartmann 2023 IEEE International Conference on Quantum Computing and Engineering (QCE …, 2023 | 6 | 2023 |
| Comprehensive Library of Variational LSE Solvers N Meyer, M Röhn, J Murauer, A Plinge, C Mutschler, DD Scherer arXiv preprint arXiv:2404.09916, 2024 | 1 | 2024 |
| Qiskit-torch-module: Fast prototyping of quantum neural networks N Meyer, C Ufrecht, M Periyasamy, A Plinge, C Mutschler, DD Scherer, ... arXiv preprint arXiv:2404.06314, 2024 | 1 | 2024 |
| Warm-Start Variational Quantum Policy Iteration N Meyer, J Murauer, A Popov, C Ufrecht, A Plinge, C Mutschler, ... arXiv preprint arXiv:2404.10546, 2024 | | 2024 |
| Reinforcement Learning@ FAU2021 C Mutschler, S Rietsch | | |
