List of seminars (2021-2022)

(the list will be constantly updated as new seminars are scheduled)


  • Title: AI for designing quantum experiments
  • Speaker: Dr. Alexey Melnkiov (Terra Quantum AG)
  • When: Thursday 21/10/21 at 6.30pm
  • Register here
  • Abstract: Quantum experiments push the envelope of our understanding of fundamental concepts in quantum physics. The designing of modern quantum experiments is difficult and often clashes with human intuition. In my talk, I will address whether a reinforcement learning agent can propose novel quantum experiments. In our works, we answer this question by considering two examples. In the first example, a reinforcement learning agent learns to create high-dimensional entangled multiphoton states. In the second example, our reinforcement learning agent proposes new unintuitive experiments leading to higher Bell-CHSH inequality violations than the best currently known setups. Our findings highlight the possibility that machine learning could have a significantly more creative role in future quantum experiments.

  • Title: A data-driven perspective on quantum matter
  • Speaker: Prof. Eliska Greplova (TU Delft)
  • When: Wednesday 17/11/21 at 4pm
  • Register here
  • Abstract: The fields of condensed matter, artificial intelligence and quantum computing have independently experienced a number of breakthroughs in the last decade. In this talk, I am going provide an illustration of their mutually beneficial overlaps through the lens of machine learning techniques. Specifically, I am going to show how Hamiltonian learning insights can bring condensed matter knowledge into the realm of quantum computing. I will illustrate this approach on quantum error correction problems. A condensed matter physics point of view on quantum error correction codes can be also readily translated into physically informed machine learning ansätze for quantum wave functions, and thus contribute to classical simulation of these models. Finally, I am going to discuss how to use quantum computational complexity insights for more successful variational optimisation.

  • Title: Quantum Reinforcement Learning with Quantum Technologies
  • Speaker: Prof. Lucas Lamata (Universidad de Sevilla)
  • When: Tuesday 14/12/21 at 4pm
  • Zoom link
  • Abstract: I will describe our theoretical work on the field of quantum reinforcement learning and its implementation in quantum technologies. I will also describe an experiment in collaboration with the Hefei quantum photonics group carrying out our previous proposal in their lab. Quantum reinforcement learning is an exciting novel paradigm which may provide advantages in state tomography and quantum learning with respect to previous algorithms.

  • Title: TBA
  • Speaker: Prof. Claudia Draxl (Humboldt-Universität Berlin)
  • When: Thursday 20/01/22 at 4pm
  • Abstract: TBA