Machine learning provides new and unprecedented insights into properties of complex quantum systems. At the same time, notions of quantum assisted machine learning may improve notions of machine learning and provide applications of near-term quantum computing. We aim at exploring such new applications and think of a rigorous theory of learning in the quantum realm, establishing what we like to call "proof pockets".

Selected group publications

• On the quantum versus classical learnability of discrete distributions
  Quantum 5, 417 (2021) 
• Training quantum embedding kernels on near-term quantum computers
  arXiv:2105.02276
• A variational toolbox for quantum multi-parameter estimation
  Nature Partner Journal Quantum Information 7 (2021)
• The effect of data encoding on the expressive power of variational quantum machine learning models
  Physical Review A 103, 032430 (2021)
• Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning
  Advances in Neural Information Processing Systems 32, Proceedings of the NeurIPS 2019 Conference (2019) 
• Stochastic gradient descent for hybrid quantum-classical optimization
  Quantum 4, 314 (2020) 
• Reinforcement learning decoders for fault-tolerant quantum computation
  Machine Learning in Science Technology 2, 025005 (2021)

• Tensor network approaches for learning non-linear dynamical laws
  Proceedings of the NeurIPS 2020 Conference (2020)