2021-07-14 00:00:00 | America/New_York

Valeria Saggio University of Vienna (Austria)

Quantum speed-ups in reinforcement learning

The field of artificial intelligence (AI) has experienced major developments over the last decade. Within AI, of particular interest is the paradigm of reinforcement learning (RL), where autonomous agents learn to accomplish a given task via feedback exchange with the world they are placed in, called an environment. Thanks to impressive advances in quantum technologies, the idea of using quantum physics to boost the performance of RL agents has been recently drawing the attention of many scientists. In my talk I will focus on the bridge between RL and quantum mechanics, and show how RL has proven amenable to quantum enhancements. I will provide an overview of the most recent results — for example, the development of agents deciding faster on their next move [1] — and I will then focus on how the learning time of an agent can be reduced using quantum physics. I will show that such a reduction can be achieved and quantified only if the agent and the environment can also interact quantum-mechanically, that is, if they can communicate via a quantum channel [2]. This idea has been implemented on a quantum platform that makes use of single photons as information carriers. The achieved speed-up in the agent’s learning time, compared to the fully classical picture, confirms the potential of quantum technologies for future RL applications. [1] Sriarunothai, T. et al. Quantum Science and Technology 4, 015014 (2018). [2] Saggio, V. et al. Nature 591, 229–233 (2021).

Speaker's Bio

Valeria Saggio is currently a post-doctoral researcher at the University of Vienna (Austria), where she obtained her Ph.D. under the supervision of Prof. Philip Walther. She carried out her Master thesis at the University of Florence (Italy) and did an internship at the Queen's University Belfast (UK) during her studies at the University of Catania (Italy), where she obtained her B.A. and M.S. in Physics. Her research has a strong experimental focus on quantum computing with photonic platforms. During her Ph.D. she worked on demonstrating efficient detection of multipartite entanglement in photonic cluster states, as well as on applications of quantum mechanics to reinforcement learning. Her research interests include working with bulk as well as integrated optics.