32 citations · 200 across the 28 of their papers we have counts for
4 papers · 2 filters
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading
Maniraman Periyasamy, Marc Hölle, Marco Wiedmann +3
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerba…
An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients
Marco Wiedmann, Marc Hölle, Maniraman Periyasamy +5
VQA have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits ma…
Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is learned automatically through trial and error in interaction with the environment.…
Cutting multi-control quantum gates with ZX calculus
Christian Ufrecht, Maniraman Periyasamy, Sebastian Rietsch +3
Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-int…